Image-assisted remote control vehicle systems and methods

ABSTRACT

Systems and methods for an image-assisted remote control vehicle are provided. An image-assisted remote control system may include a motion controlled device such as an image-assisted remote control vehicle and a remote control device. The motion controlled device may include one or more imaging modules, such as a thermal imaging module and/or a non-thermal imaging module. An infrared image of a part of a track may be captured using the infrared imaging module. A boundary of the track may be detected using the infrared image. The operation of the vehicle may be modified based on the boundary. Modifying the operation of the vehicle in this way may provide a remote controlled vehicle with the ease of use of a slot car track set, the flexibility of remote control cars and the ability for a user to design their own track.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Patent Application No. 62/050,707 filed Sep. 15, 2014 and entitled “IMAGE-ASSISTED REMOTE CONTROL VEHICLE” which is hereby incorporated by reference in its entirety.

This application is a continuation-in-part of U.S. patent application Ser. No. 14/101,245 filed Dec. 9, 2013 and entitled “LOW POWER AND SMALL FORM FACTOR INFRARED IMAGING” which is hereby incorporated by reference in its entirety.

U.S. patent application Ser. No. 14/101,245 is a continuation of International Patent Application No. PCT/US2012/041744 filed Jun. 8, 2012 and entitled “LOW POWER AND SMALL FORM FACTOR INFRARED IMAGING” which is hereby incorporated by reference in its entirety.

International Patent Application No. PCT/US2012/041744 claims the benefit of U.S. Provisional Patent Application No. 61/656,889 filed Jun. 7, 2012 and entitled “LOW POWER AND SMALL FORM FACTOR INFRARED IMAGING” which is hereby incorporated by reference in its entirety.

International Patent Application No. PCT/US2012/041744 claims the benefit of U.S. Provisional Patent Application No. 61/495,873 filed Jun. 10, 2011 and entitled “INFRARED CAMERA PACKAGING SYSTEMS AND METHODS” which is hereby incorporated by reference in its entirety.

International Patent Application No. PCT/US2012/041744 claims the benefit of U.S. Provisional Patent Application No. 61/495,879 filed Jun. 10, 2011 and entitled “INFRARED CAMERA SYSTEM ARCHITECTURES” which is hereby incorporated by reference in its entirety.

International Patent Application No. PCT/US2012/041744 claims the benefit of U.S. Provisional Patent Application No. 61/495,888 filed Jun. 10, 2011 and entitled “INFRARED CAMERA CALIBRATION TECHNIQUES” which is hereby incorporated by reference in its entirety.

This application is a continuation-in-part of U.S. patent application Ser. No. 14/101,258 filed Dec. 9, 2013 and entitled “INFRARED CAMERA SYSTEM ARCHITECTURES” which is hereby incorporated by reference in its entirety.

U.S. patent application Ser. No. 14/101,258 is a continuation of International Patent Application No. PCT/US2012/041739 filed Jun. 8, 2012 and entitled “INFRARED CAMERA SYSTEM ARCHITECTURES” which is hereby incorporated by reference in its entirety.

International Patent Application No. PCT/US2012/041739 claims the benefit of U.S. Provisional Patent Application No. 61/495,873 filed Jun. 10, 2011 and entitled “INFRARED CAMERA PACKAGING SYSTEMS AND METHODS” which is hereby incorporated by reference in its entirety.

International Patent Application No. PCT/US2012/041739 claims the benefit of U.S. Provisional Patent Application No. 61/495,879 filed Jun. 10, 2011 and entitled “INFRARED CAMERA SYSTEM ARCHITECTURES” which is hereby incorporated by reference in its entirety.

International Patent Application No. PCT/US2012/041739 claims the benefit of U.S. Provisional Patent Application No. 61/495,888 filed Jun. 10, 2011 and entitled “INFRARED CAMERA CALIBRATION TECHNIQUES” which is hereby incorporated by reference in its entirety.

This application is a continuation-in-part of U.S. patent application Ser. No. 14/138,058 filed Dec. 21, 2013 and entitled “COMPACT MULTI-SPECTRUM IMAGING WITH FUSION” which is hereby incorporated by reference in its entirety.

U.S. patent application Ser. No. 14/138,058 claims the benefit of U.S. Provisional Patent Application No. 61/748,018 filed Dec. 31, 2012 and entitled “COMPACT MULTI-SPECTRUM IMAGING WITH FUSION” which is hereby incorporated by reference in its entirety.

This application is a continuation-in-part of U.S. patent application Ser. No. 14/506,430 filed Oct. 3, 2014 and entitled “WEARABLE IMAGING DEVICES, SYSTEMS, AND METHODS” which is hereby incorporated by reference in its entirety.

This application is a continuation-in-part of U.S. patent application Ser. No. 14/138,040 filed Dec. 21, 2013 and entitled “TIME SPACED INFRARED IMAGE ENHANCEMENT” which is hereby incorporated by reference in its entirety.

U.S. patent application Ser. No. 14/138,040 claims the benefit of U.S. Provisional Patent Application No. 61/792,582 filed Mar. 15, 2013 and entitled “TIME SPACED INFRARED IMAGE ENHANCEMENT” which is hereby incorporated by reference in its entirety.

U.S. patent application Ser. No. 14/138,040 also claims the benefit of U.S. Provisional Patent Application No. 61/746,069 filed Dec. 26, 2012 and entitled “TIME SPACED INFRARED IMAGE ENHANCEMENT” which is hereby incorporated by reference in its entirety.

This application is a continuation-in-part of U.S. patent application Ser. No. 14/138,052 filed Dec. 21, 2013 and entitled “INFRARED IMAGING ENHANCEMENT WITH FUSION” which is hereby incorporated by reference in its entirety.

U.S. patent application Ser. No. 14/138,052 claims the benefit of U.S. Provisional Patent Application No. 61/793,952 filed Mar. 15, 2013 and entitled “INFRARED IMAGING ENHANCEMENT WITH FUSION” which is hereby incorporated by reference in its entirety.

U.S. patent application Ser. No. 14/138,052 also claims the benefit of U.S. Provisional Patent Application No. 61/746,074 filed Dec. 26, 2012 and entitled “INFRARED IMAGING ENHANCEMENT WITH FUSION” which is hereby incorporated by reference in its entirety.

U.S. patent application Ser. No. 12/477,828 filed Jun. 3, 2009 and entitled “INFRARED CAMERA SYSTEMS AND METHODS FOR DUAL SENSOR APPLICATIONS” is hereby incorporated by reference in its entirety.

U.S. patent application Ser. No. 12/766,739 filed Apr. 23, 2010 and entitled “INFRARED RESOLUTION AND CONTRAST ENHANCEMENT WITH FUSION” is hereby incorporated by reference in its entirety.

U.S. Provisional Patent Application No. 61/473,207 filed Apr. 8, 2011 and entitled “INFRARED RESOLUTION AND CONTRAST ENHANCEMENT WITH FUSION” is hereby incorporated by reference in its entirety.

International Patent Application No. PCT/EP2011/056432 filed Apr. 21, 2011 and entitled “INFRARED RESOLUTION AND CONTRAST ENHANCEMENT WITH FUSION” is hereby incorporated by reference in its entirety.

U.S. patent application Ser. No. 13/105,765 filed May 11, 2011 and entitled “INFRARED RESOLUTION AND CONTRAST ENHANCEMENT WITH FUSION” is hereby incorporated by reference in its entirety.

U.S. Provisional Patent Application No. 61/545,056 filed Oct. 7, 2011 and entitled “NON-UNIFORMITY CORRECTION TECHNIQUES FOR INFRARED IMAGING DEVICES” is hereby incorporated by reference in its entirety.

U.S. patent application Ser. No. 13/437,645 filed Apr. 2, 2012 and entitled “INFRARED RESOLUTION AND CONTRAST ENHANCEMENT WITH FUSION” is hereby incorporated by reference in its entirety.

International Patent Application No. PCT/US2012/041749 filed Jun. 8, 2012 and entitled “NON-UNIFORMITY CORRECTION TECHNIQUES FOR INFRARED IMAGING DEVICES” is hereby incorporated by reference in its entirety.

U.S. patent application Ser. No. 14/099,818 filed Dec. 6, 2013 and entitled “NON-UNIFORMITY CORRECTION TECHNIQUES FOR INFRARED IMAGING DEVICES” is hereby incorporated by reference in its entirety.

U.S. patent application Ser. No. 14/299,987 filed Jun. 9, 2014 and entitled “INFRARED CAMERA SYSTEMS AND METHODS FOR DUAL SENSOR APPLICATIONS” is hereby incorporated by reference in its entirety.

TECHNICAL FIELD

One or more embodiments of the invention relate generally to infrared imaging devices and more particularly, for example, to image-assisted remote control vehicles.

BACKGROUND

Remote control vehicles such as remote controlled cars typically include a vehicle operated by a user with a remote control device. These remote control vehicles can include freely controllable vehicles or slot car vehicles.

Freely controllable remote control vehicles may offer too much flexibility in their directional control and speed for users, particularly young children and users accustomed to video game controls in which the vehicles are confined to a track, to enjoy. For example, users may find it difficult to control the remote control vehicle to race with other remote control vehicles because it is difficult to stay on the track. On the other hand, slot cars that are guided by a groove or a slot on a track can have undesirably limited flexibility because they lack directional control. Thus, users typically get bored soon after they start playing with the slot cars.

It would therefore be desirable to provide improved remote control vehicles.

SUMMARY

Various embodiments are disclosed for systems and methods for image-assisted remote control vehicles. An image-assisted remote control vehicle system may include a motion controlled device such as an image-assisted remote control vehicle and a remote control device (remote control) with which the motion controlled device can be operated. The motion controlled device may include one or more imaging modules such as a thermal imaging module and/or a non-thermal imaging module.

An image-assisted remote control vehicle system may be a system for remote control vehicles assisted by infrared imaging and/or an image-assisted remote control device such as a motion controlled device having a mounted infrared imaging module. The motion controlled device may be a remote control vehicle that travels in land, water, and/or air, such as an automobile, boat, submarine, helicopter, plane, etc.

The image-assisted remote control vehicle system may include a remote control that controls the vehicle. The remote control may communicate wirelessly with the vehicle. The remote control includes input components, such as physical controls or a touch screen, for a user to input a direction and speed for the vehicle. In some embodiments, the remote control includes a mobile device such as a mobile phone, a tablet, etc.

Images such as infrared images captured using the infrared imaging module may be processed to detect boundaries, such as a boundary of a track. For example, infrared images may be captured using the infrared imaging module during operation of the motion controlled device. The boundary may be detected by detecting changes between sequentially captured images (e.g., due to a track boundary appearing in an image that was not present in a previously captured image) and/or by directly detecting the boundary in a captured image (e.g., by detecting an edge in the image or by detecting a temperature, color, or other known characteristic of the boundary).

The image-assisted remote control vehicle system may include one or more processors, and each of the one or more processors may be disposed within the vehicle or on the remote control. The one or more processors process the infrared images and detect the boundary. When a boundary is detected, the one or more processors may operate to modify the operation of the vehicle based on the boundary. The one or more processors may operate, for example, to reduce a speed of the vehicle and/or steer away from the boundary when the vehicle approaches the boundary. In this way, the infrared imaging module can be used to assist the vehicle stay within the boundary.

In one embodiment, the image-assisted remote control vehicle system may be implemented as a remote control vehicle that is assisted by infrared imaging to stay within a lane of a track. For example, the one or more processors may be configured to determine a lane defined by boundaries on each side of the vehicle. The one or more processors may modify operation of the vehicle by reducing the angle between the lane and the direction of travel to keep the vehicle within the lane.

In one embodiment, a system is provided that includes a remote control vehicle; an infrared imaging module mounted on the remote control vehicle, in which the infrared imaging module is configured to capture an infrared image of a part of a track; and a processor configured to communicate with the infrared imaging module, wherein the processor is configured to: detect a boundary of the track using the infrared image; and modify an operation of the remote control vehicle based on the boundary.

In one embodiment, a method is provided that includes capturing an infrared image of a part of the track with an infrared imaging module on a remote control vehicle; detecting a boundary of the track in the infrared image; and modifying an operation of the remote control vehicle based on the boundary.

In some embodiments, the method may also include determining, based on the infrared image, a distance between the boundary and the remote control vehicle; determining, based on the infrared image, a speed at which the remote control vehicle is approaching the boundary; and determining, based on the infrared image, an orientation of the boundary relative to a direction of travel of the remote control vehicle.

The image-assisted remote control vehicle may include a housing structure in which an imaging module such as an infrared-imaging module is located. The housing structure may be a detachable structure that can be removed from the remote control vehicle or can be integrally formed with the remote control vehicle.

The scope of the invention is defined by the claims, which are incorporated into this section by reference. A more complete understanding of embodiments of the invention will be afforded to those skilled in the art, as well as a realization of additional advantages thereof, by a consideration of the following detailed description of one or more embodiments. Reference will be made to the appended sheets of drawings that will first be described briefly.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an infrared imaging module configured to be implemented in a host device in accordance with an embodiment of the disclosure.

FIG. 2 illustrates an assembled infrared imaging module in accordance with an embodiment of the disclosure.

FIG. 3 illustrates an exploded view of an infrared imaging module juxtaposed over a socket in accordance with an embodiment of the disclosure.

FIG. 4 illustrates a block diagram of an infrared sensor assembly including an array of infrared sensors in accordance with an embodiment of the disclosure.

FIG. 5 illustrates a flow diagram of various operations to determine NUC terms in accordance with an embodiment of the disclosure.

FIG. 6 illustrates differences between neighboring pixels in accordance with an embodiment of the disclosure.

FIG. 7 illustrates a flat field correction technique in accordance with an embodiment of the disclosure.

FIG. 8 illustrates various image processing techniques of FIG. 5 and other operations applied in an image processing pipeline in accordance with an embodiment of the disclosure.

FIG. 9 illustrates a temporal noise reduction process in accordance with an embodiment of the disclosure.

FIG. 10 illustrates particular implementation details of several processes of the image processing pipeline of FIG. 6 in accordance with an embodiment of the disclosure.

FIG. 11 illustrates spatially correlated FPN in a neighborhood of pixels in accordance with an embodiment of the disclosure.

FIG. 12 illustrates a block diagram of another implementation of an infrared sensor assembly including an array of infrared sensors and a low-dropout regulator in accordance with an embodiment of the disclosure.

FIG. 13 illustrates a circuit diagram of a portion of the infrared sensor assembly of FIG. 12 in accordance with an embodiment of the disclosure.

FIG. 14 illustrates a block diagram of a host system having an infrared imaging module and a visible light camera in accordance with an embodiment of the disclosure.

FIG. 15 illustrates an example thermal image that may be captured using an infrared imaging module and analyzed by a processor in accordance with an embodiment of the disclosure.

FIG. 16 illustrates a process for combining thermal images and visible light images in accordance with an embodiment of the disclosure.

FIG. 17 illustrates a block diagram of a host system that is implemented as an image-assisted remote control vehicle with an infrared imaging module in accordance with an embodiment of the disclosure.

FIG. 18 illustrates an image-assisted remote control vehicle system in accordance with an embodiment of the disclosure.

FIG. 19 illustrates a diagram of an implementation of an image-assisted remote control vehicle system showing an image-assisted remote control vehicle on a track in accordance with an embodiment of the disclosure.

FIG. 20 illustrates a process for operating an image-assisted remote control vehicle system in accordance with an embodiment of the disclosure.

Embodiments of the invention and their advantages are best understood by referring to the detailed description that follows. It should be appreciated that like reference numerals are used to identify like elements illustrated in one or more of the figures.

DETAILED DESCRIPTION

FIG. 1 illustrates an infrared imaging module 100 (e.g., an infrared camera or an infrared imaging device) configured to be implemented in a host device 102 in accordance with an embodiment of the disclosure. Infrared imaging module 100 may be implemented, for one or more embodiments, with a small form factor and in accordance with wafer level packaging techniques or other packaging techniques.

In one embodiment, infrared imaging module 100 may be configured to be implemented in a host device 102, such as a remote control vehicle, a mobile telephone, a tablet computing device, a laptop computing device, a personal digital assistant, a visible light camera, a music player, a wearable imaging device, or any other appropriate device. In this regard, infrared imaging module 100 may be used to provide infrared imaging features to host device 102. For example, infrared imaging module 100 may be configured to capture, process, and/or otherwise manage infrared images and provide such infrared images to host device 102 for use in any desired fashion (e.g., for further processing, to store in memory, to display, to use by various applications running on host device 102, to export to other devices, or other uses).

In various embodiments, infrared imaging module 100 may be configured to operate at low voltage levels and over a wide temperature range. For example, in one embodiment, infrared imaging module 100 may operate using a power supply of approximately 2.4 volts, 2.5 volts, 2.8 volts, or lower voltages, and operate over a temperature range of approximately −20 degrees C. to approximately +60 degrees C. (e.g., providing a suitable dynamic range and performance over an environmental temperature range of approximately 80 degrees C.). In one embodiment, by operating infrared imaging module 100 at low voltage levels, infrared imaging module 100 may experience reduced amounts of self heating in comparison with other types of infrared imaging devices. As a result, infrared imaging module 100 may be operated with reduced measures to compensate for such self heating.

As shown in FIG. 1, host device 102 may include a socket 104, a shutter 105, motion sensors 194, a processor 195, a memory 196, a display 197, and/or other components 198. Socket 104 may be configured to receive infrared imaging module 100 as identified by arrow 101. In this regard, FIG. 2 illustrates infrared imaging module 100 assembled in socket 104 in accordance with an embodiment of the disclosure.

Motion sensors 194 may be implemented by one or more accelerometers, gyroscopes, or other appropriate devices that may be used to detect movement of host device 102. Motion sensors 194 may be monitored by and provide information to processing module 160 or processor 195 to detect motion. In various embodiments, motion sensors 194 may be implemented as part of host device 102 (as shown in FIG. 1), infrared imaging module 100, or other devices attached to or otherwise interfaced with host device 102.

Processor 195 may be implemented as any appropriate processing device (e.g., logic device, microcontroller, processor, application specific integrated circuit (ASIC), or other device) that may be used by host device 102 to execute appropriate instructions, such as software instructions provided in memory 196. Display 197 may be used to display captured and/or processed infrared images and/or other images, data, and information. Other components 198 may be used to implement any features of host device 102 as may be desired for various applications (e.g., clocks, temperature sensors, a visible light camera, or other components). In addition, a machine readable medium 193 may be provided for storing non-transitory instructions for loading into memory 196 and execution by processor 195.

In various embodiments, infrared imaging module 100 and socket 104 may be implemented for mass production to facilitate high volume applications, such as for implementation in mobile telephones or other devices (e.g., requiring small form factors). In one embodiment, the combination of infrared imaging module 100 and socket 104 may exhibit overall dimensions of approximately 8.5 mm by 8.5 mm by 5.9 mm while infrared imaging module 100 is installed in socket 104.

FIG. 3 illustrates an exploded view of infrared imaging module 100 juxtaposed over socket 104 in accordance with an embodiment of the disclosure. Infrared imaging module 100 may include a lens barrel 110, a housing 120, an infrared sensor assembly 128, a circuit board 170, a base 150, and a processing module 160.

Lens barrel 110 may at least partially enclose an optical element 180 (e.g., a lens) which is partially visible in FIG. 3 through an aperture 112 in lens barrel 110. Lens barrel 110 may include a substantially cylindrical extension 114 which may be used to interface lens barrel 110 with an aperture 122 in housing 120.

Infrared sensor assembly 128 may be implemented, for example, with a cap 130 (e.g., a lid) mounted on a substrate 140. Infrared sensor assembly 128 may include a plurality of infrared sensors 132 (e.g., infrared detectors) implemented in an array or other fashion on substrate 140 and covered by cap 130. For example, in one embodiment, infrared sensor assembly 128 may be implemented as a focal plane array (FPA). Such a focal plane array may be implemented, for example, as a vacuum package assembly (e.g., sealed by cap 130 and substrate 140). In one embodiment, infrared sensor assembly 128 may be implemented as a wafer level package (e.g., infrared sensor assembly 128 may be singulated from a set of vacuum package assemblies provided on a wafer). In one embodiment, infrared sensor assembly 128 may be implemented to operate using a power supply of approximately 2.4 volts, 2.5 volts, 2.8 volts, or similar voltages.

Infrared sensors 132 may be configured to detect infrared radiation (e.g., infrared energy) from a target scene including, for example, mid wave infrared wave bands (MWIR), long wave infrared wave bands (LWIR), and/or other thermal imaging bands as may be desired in particular implementations. In one embodiment, infrared sensor assembly 128 may be provided in accordance with wafer level packaging techniques.

Infrared sensors 132 may be implemented, for example, as microbolometers or other types of thermal imaging infrared sensors arranged in any desired array pattern to provide a plurality of pixels. In one embodiment, infrared sensors 132 may be implemented as vanadium oxide (VOx) detectors with a 17 μm pixel pitch. In various embodiments, arrays of approximately 32 by 32 infrared sensors 132, approximately 64 by 64 infrared sensors 132, approximately 80 by 64 infrared sensors 132, or other array sizes may be used.

Substrate 140 may include various circuitry including, for example, a read out integrated circuit (ROIC) with dimensions less than approximately 5.5 mm by 5.5 mm in one embodiment. Substrate 140 may also include bond pads 142 that may be used to contact complementary connections positioned on inside surfaces of housing 120 when infrared imaging module 100 is assembled as shown in FIGS. 5A, 5B, and 5C. In one embodiment, the ROIC may be implemented with low-dropout regulators (LDO) to perform voltage regulation to reduce power supply noise introduced to infrared sensor assembly 128 and thus provide an improved power supply rejection ratio (PSRR). Moreover, by implementing the LDO with the ROIC (e.g., within a wafer level package), less die area may be consumed and fewer discrete die (or chips) are needed.

FIG. 4 illustrates a block diagram of infrared sensor assembly 128 including an array of infrared sensors 132 in accordance with an embodiment of the disclosure. In the illustrated embodiment, infrared sensors 132 are provided as part of a unit cell array of a ROIC 402. ROIC 402 includes bias generation and timing control circuitry 404, column amplifiers 405, a column multiplexer 406, a row multiplexer 408, and an output amplifier 410. Image frames (e.g., thermal images) captured by infrared sensors 132 may be provided by output amplifier 410 to processing module 160, processor 195, and/or any other appropriate components to perform various processing techniques described herein. Although an 8 by 8 array is shown in FIG. 4, any desired array configuration may be used in other embodiments. Further descriptions of ROICs and infrared sensors (e.g., microbolometer circuits) may be found in U.S. Pat. No. 6,028,309 issued Feb. 22, 2000, which is incorporated herein by reference in its entirety.

Infrared sensor assembly 128 may capture images (e.g., image frames) and provide such images from its ROIC at various rates. Processing module 160 may be used to perform appropriate processing of captured infrared images and may be implemented in accordance with any appropriate architecture. In one embodiment, processing module 160 may be implemented as an ASIC. In this regard, such an ASIC may be configured to perform image processing with high performance and/or high efficiency. In another embodiment, processing module 160 may be implemented with a general purpose central processing unit (CPU) which may be configured to execute appropriate software instructions to perform image processing, coordinate and perform image processing with various image processing blocks, coordinate interfacing between processing module 160 and host device 102, and/or other operations. In yet another embodiment, processing module 160 may be implemented with a field programmable gate array (FPGA). Processing module 160 may be implemented with other types of processing and/or logic circuits in other embodiments as would be understood by one skilled in the art.

In these and other embodiments, processing module 160 may also be implemented with other components where appropriate, such as, volatile memory, non-volatile memory, and/or one or more interfaces (e.g., infrared detector interfaces, inter-integrated circuit (I2C) interfaces, mobile industry processor interfaces (MIPI), joint test action group (JTAG) interfaces (e.g., IEEE 1149.1 standard test access port and boundary-scan architecture), and/or other interfaces).

In some embodiments, infrared imaging module 100 may further include one or more actuators 199 which may be used to adjust the focus of infrared image frames captured by infrared sensor assembly 128. For example, actuators 199 may be used to move optical element 180, infrared sensors 132, and/or other components relative to each other to selectively focus and defocus infrared image frames in accordance with techniques described herein. Actuators 199 may be implemented in accordance with any type of motion-inducing system or mechanism, and may positioned at any location within or external to infrared imaging module 100 as appropriate for different applications.

When infrared imaging module 100 is assembled, housing 120 may substantially enclose infrared sensor assembly 128, base 150, and processing module 160. Housing 120 may facilitate connection of various components of infrared imaging module 100. For example, in one embodiment, housing 120 may provide electrical connections 126 to connect various components as further described.

Electrical connections 126 (e.g., conductive electrical paths, traces, or other types of connections) may be electrically connected with bond pads 142 when infrared imaging module 100 is assembled. In various embodiments, electrical connections 126 may be embedded in housing 120, provided on inside surfaces of housing 120, and/or otherwise provided by housing 120. Electrical connections 126 may terminate in connections 124 protruding from the bottom surface of housing 120 as shown in FIG. 3. Connections 124 may connect with circuit board 170 when infrared imaging module 100 is assembled (e.g., housing 120 may rest atop circuit board 170 in various embodiments). Processing module 160 may be electrically connected with circuit board 170 through appropriate electrical connections. As a result, infrared sensor assembly 128 may be electrically connected with processing module 160 through, for example, conductive electrical paths provided by: bond pads 142, complementary connections on inside surfaces of housing 120, electrical connections 126 of housing 120, connections 124, and circuit board 170. Advantageously, such an arrangement may be implemented without requiring wire bonds to be provided between infrared sensor assembly 128 and processing module 160.

In various embodiments, electrical connections 126 in housing 120 may be made from any desired material (e.g., copper or any other appropriate conductive material). In one embodiment, electrical connections 126 may aid in dissipating heat from infrared imaging module 100.

Other connections may be used in other embodiments. For example, in one embodiment, sensor assembly 128 may be attached to processing module 160 through a ceramic board that connects to sensor assembly 128 by wire bonds and to processing module 160 by a ball grid array (BGA). In another embodiment, sensor assembly 128 may be mounted directly on a rigid flexible board and electrically connected with wire bonds, and processing module 160 may be mounted and connected to the rigid flexible board with wire bonds or a BGA.

The various implementations of infrared imaging module 100 and host device 102 set forth herein are provided for purposes of example, rather than limitation. In this regard, any of the various techniques described herein may be applied to any infrared camera system, infrared imager, or other device for performing infrared/thermal imaging.

Substrate 140 of infrared sensor assembly 128 may be mounted on base 150. In various embodiments, base 150 (e.g., a pedestal) may be made, for example, of copper formed by metal injection molding (MIM) and provided with a black oxide or nickel-coated finish. In various embodiments, base 150 may be made of any desired material, such as for example zinc, aluminum, or magnesium, as desired for a given application and may be formed by any desired applicable process, such as for example aluminum casting, MIM, or zinc rapid casting, as may be desired for particular applications. In various embodiments, base 150 may be implemented to provide structural support, various circuit paths, thermal heat sink properties, and other features where appropriate. In one embodiment, base 150 may be a multi-layer structure implemented at least in part using ceramic material.

In various embodiments, circuit board 170 may receive housing 120 and thus may physically support the various components of infrared imaging module 100. In various embodiments, circuit board 170 may be implemented as a printed circuit board (e.g., an FR4 circuit board or other types of circuit boards), a rigid or flexible interconnect (e.g., tape or other type of interconnects), a flexible circuit substrate, a flexible plastic substrate, or other appropriate structures. In various embodiments, base 150 may be implemented with the various features and attributes described for circuit board 170, and vice versa.

Socket 104 may include a cavity 106 configured to receive infrared imaging module 100 (e.g., as shown in the assembled view of FIG. 2). Infrared imaging module 100 and/or socket 104 may include appropriate tabs, arms, pins, fasteners, or any other appropriate engagement members which may be used to secure infrared imaging module 100 to or within socket 104 using friction, tension, adhesion, and/or any other appropriate manner. Socket 104 may include engagement members 107 that may engage surfaces 109 of housing 120 when infrared imaging module 100 is inserted into a cavity 106 of socket 104. Other types of engagement members may be used in other embodiments.

Infrared imaging module 100 may be electrically connected with socket 104 through appropriate electrical connections (e.g., contacts, pins, wires, or any other appropriate connections). For example, socket 104 may include electrical connections 108 which may contact corresponding electrical connections of infrared imaging module 100 (e.g., interconnect pads, contacts, or other electrical connections on side or bottom surfaces of circuit board 170, bond pads 142 or other electrical connections on base 150, or other connections). Electrical connections 108 may be made from any desired material (e.g., copper or any other appropriate conductive material). In one embodiment, electrical connections 108 may be mechanically biased to press against electrical connections of infrared imaging module 100 when infrared imaging module 100 is inserted into cavity 106 of socket 104. In one embodiment, electrical connections 108 may at least partially secure infrared imaging module 100 in socket 104. Other types of electrical connections may be used in other embodiments.

Socket 104 may be electrically connected with host device 102 through similar types of electrical connections. For example, in one embodiment, host device 102 may include electrical connections (e.g., soldered connections, snap-in connections, or other connections) that connect with electrical connections 108 passing through apertures 190. In various embodiments, such electrical connections may be made to the sides and/or bottom of socket 104.

Various components of infrared imaging module 100 may be implemented with flip chip technology which may be used to mount components directly to circuit boards without the additional clearances typically needed for wire bond connections. Flip chip connections may be used, as an example, to reduce the overall size of infrared imaging module 100 for use in compact small form factor applications. For example, in one embodiment, processing module 160 may be mounted to circuit board 170 using flip chip connections. For example, infrared imaging module 100 may be implemented with such flip chip configurations.

In various embodiments, infrared imaging module 100 and/or associated components may be implemented in accordance with various techniques (e.g., wafer level packaging techniques) as set forth in U.S. patent application Ser. No. 12/844,124 filed Jul. 27, 2010, and U.S. Provisional Patent Application No. 61/469,651 filed Mar. 30, 2011, which are incorporated herein by reference in their entirety. Furthermore, in accordance with one or more embodiments, infrared imaging module 100 and/or associated components may be implemented, calibrated, tested, and/or used in accordance with various techniques, such as for example as set forth in U.S. Pat. No. 7,470,902 issued Dec. 30, 2008, U.S. Pat. No. 6,028,309 issued Feb. 22, 2000, U.S. Pat. No. 6,812,465 issued Nov. 2, 2004, U.S. Pat. No. 7,034,301 issued Apr. 25, 2006, U.S. Pat. No. 7,679,048 issued Mar. 16, 2010, U.S. Pat. No. 7,470,904 issued Dec. 30, 2008, U.S. patent application Ser. No. 12/202,880 filed Sep. 2, 2008, and U.S. patent application Ser. No. 12/202,896 filed Sep. 2, 2008, which are incorporated herein by reference in their entirety.

Referring again to FIG. 1, in various embodiments, host device 102 may include shutter 105. In this regard, shutter 105 may be selectively positioned over socket 104 (e.g., as identified by arrows 103) while infrared imaging module 100 is installed therein. In this regard, shutter 105 may be used, for example, to protect infrared imaging module 100 when not in use. Shutter 105 may also be used as a temperature reference as part of a calibration process (e.g., a NUC process or other calibration processes) for infrared imaging module 100 as would be understood by one skilled in the art.

In various embodiments, shutter 105 may be made from various materials such as, for example, polymers, glass, aluminum (e.g., painted or anodized) or other materials. In various embodiments, shutter 105 may include one or more coatings to selectively filter electromagnetic radiation and/or adjust various optical properties of shutter 105 (e.g., a uniform blackbody coating or a reflective gold coating).

In another embodiment, shutter 105 may be fixed in place to protect infrared imaging module 100 at all times. In this case, shutter 105 or a portion of shutter 105 may be made from appropriate materials (e.g., polymers or infrared transmitting materials such as silicon, germanium, zinc selenide, or chalcogenide glasses) that do not substantially filter desired infrared wavelengths. In another embodiment, a shutter may be implemented as part of infrared imaging module 100 (e.g., within or as part of a lens barrel or other components of infrared imaging module 100), as would be understood by one skilled in the art.

Alternatively, in another embodiment, a shutter (e.g., shutter 105 or other type of external or internal shutter) need not be provided, but rather a NUC process or other type of calibration may be performed using shutterless techniques. In another embodiment, a NUC process or other type of calibration using shutterless techniques may be performed in combination with shutter-based techniques.

Infrared imaging module 100 and host device 102 may be implemented in accordance with any of the various techniques set forth in U.S. Provisional Patent Application No. 61/495,873 filed Jun. 10, 2011, U.S. Provisional Patent Application No. 61/495,879 filed Jun. 10, 2011, and U.S. Provisional Patent Application No. 61/495,888 filed Jun. 10, 2011, which are incorporated herein by reference in their entirety.

In various embodiments, the components of host device 102 and/or infrared imaging module 100 may be implemented as a local or distributed system with components in communication with each other over wired and/or wireless networks. Accordingly, the various operations identified in this disclosure may be performed by local and/or remote components as may be desired in particular implementations.

FIG. 5 illustrates a flow diagram of various operations to determine NUC terms in accordance with an embodiment of the disclosure. In some embodiments, the operations of FIG. 5 may be performed by processing module 160 or processor 195 (both also generally referred to as a processor) operating on image frames captured by infrared sensors 132.

In block 505, infrared sensors 132 begin capturing image frames of a scene. Typically, the scene will be the real world environment in which host device 102 is currently located. In this regard, shutter 105 (if optionally provided) may be opened to permit infrared imaging module to receive infrared radiation from the scene. Infrared sensors 132 may continue capturing image frames during all operations shown in FIG. 5. In this regard, the continuously captured image frames may be used for various operations as further discussed. In one embodiment, the captured image frames may be temporally filtered (e.g., in accordance with the process of block 826 further described herein with regard to FIG. 8) and be processed by other terms (e.g., factory gain terms 812, factory offset terms 816, previously determined NUC terms 817, column FPN terms 820, and row FPN terms 824 as further described herein with regard to FIG. 8) before they are used in the operations shown in FIG. 5.

In block 510, a NUC process initiating event is detected. In one embodiment, the NUC process may be initiated in response to physical movement of host device 102. Such movement may be detected, for example, by motion sensors 194 which may be polled by a processor. In one example, a user may move host device 102 in a particular manner, such as by intentionally waving host device 102 back and forth in an “erase” or “swipe” movement. In this regard, the user may move host device 102 in accordance with a predetermined speed and direction (velocity), such as in an up and down, side to side, or other pattern to initiate the NUC process. In this example, the use of such movements may permit the user to intuitively operate host device 102 to simulate the “erasing” of noise in captured image frames.

In another example, a NUC process may be initiated by host device 102 if motion exceeding a threshold value is exceeded (e.g., motion greater than expected for ordinary use). It is contemplated that any desired type of spatial translation of host device 102 may be used to initiate the NUC process.

In yet another example, a NUC process may be initiated by host device 102 if a minimum time has elapsed since a previously performed NUC process. In a further example, a NUC process may be initiated by host device 102 if infrared imaging module 100 has experienced a minimum temperature change since a previously performed NUC process. In a still further example, a NUC process may be continuously initiated and repeated.

In block 515, after a NUC process initiating event is detected, it is determined whether the NUC process should actually be performed. In this regard, the NUC process may be selectively initiated based on whether one or more additional conditions are met. For example, in one embodiment, the NUC process may not be performed unless a minimum time has elapsed since a previously performed NUC process. In another embodiment, the NUC process may not be performed unless infrared imaging module 100 has experienced a minimum temperature change since a previously performed NUC process. Other criteria or conditions may be used in other embodiments. If appropriate criteria or conditions have been met, then the flow diagram continues to block 520. Otherwise, the flow diagram returns to block 505.

In the NUC process, blurred image frames may be used to determine NUC terms which may be applied to captured image frames to correct for FPN. As discussed, in one embodiment, the blurred image frames may be obtained by accumulating multiple image frames of a moving scene (e.g., captured while the scene and/or the thermal imager is in motion). In another embodiment, the blurred image frames may be obtained by defocusing an optical element or other component of the thermal imager.

Accordingly, in block 520 a choice of either approach is provided. If the motion-based approach is used, then the flow diagram continues to block 525. If the defocus-based approach is used, then the flow diagram continues to block 530.

Referring now to the motion-based approach, in block 525 motion is detected. For example, in one embodiment, motion may be detected based on the image frames captured by infrared sensors 132. In this regard, an appropriate motion detection process (e.g., an image registration process, a frame-to-frame difference calculation, or other appropriate process) may be applied to captured image frames to determine whether motion is present (e.g., whether static or moving image frames have been captured). For example, in one embodiment, it can be determined whether pixels or regions around the pixels of consecutive image frames have changed more than a user defined amount (e.g., a percentage and/or threshold value). If at least a given percentage of pixels have changed by at least the user defined amount, then motion will be detected with sufficient certainty to proceed to block 535.

In another embodiment, motion may be determined on a per pixel basis, in which only pixels that exhibit significant changes are accumulated to provide the blurred image frame. For example, counters may be provided for each pixel and used to ensure that the same number of pixel values are accumulated for each pixel, or used to average the pixel values based on the number of pixel values actually accumulated for each pixel. Other types of image-based motion detection may be performed such as performing a Radon transform.

In another embodiment, motion may be detected based on data provided by motion sensors 194. In one embodiment, such motion detection may include detecting whether host device 102 is moving along a relatively straight trajectory through space. For example, if host device 102 is moving along a relatively straight trajectory, then it is possible that certain objects appearing in the imaged scene may not be sufficiently blurred (e.g., objects in the scene that may be aligned with or moving substantially parallel to the straight trajectory). Thus, in such an embodiment, the motion detected by motion sensors 194 may be conditioned on host device 102 exhibiting, or not exhibiting, particular trajectories.

In yet another embodiment, both a motion detection process and motion sensors 194 may be used. Thus, using any of these various embodiments, a determination can be made as to whether or not each image frame was captured while at least a portion of the scene and host device 102 were in motion relative to each other (e.g., which may be caused by host device 102 moving relative to the scene, at least a portion of the scene moving relative to host device 102, or both).

It is expected that the image frames for which motion was detected may exhibit some secondary blurring of the captured scene (e.g., blurred thermal image data associated with the scene) due to the thermal time constants of infrared sensors 132 (e.g., microbolometer thermal time constants) interacting with the scene movement.

In block 535, image frames for which motion was detected are accumulated. For example, if motion is detected for a continuous series of image frames, then the image frames of the series may be accumulated. As another example, if motion is detected for only some image frames, then the non-moving image frames may be skipped and not included in the accumulation. Thus, a continuous or discontinuous set of image frames may be selected to be accumulated based on the detected motion.

In block 540, the accumulated image frames are averaged to provide a blurred image frame. Because the accumulated image frames were captured during motion, it is expected that actual scene information will vary between the image frames and thus cause the scene information to be further blurred in the resulting blurred image frame (block 545).

In contrast, FPN (e.g., caused by one or more components of infrared imaging module 100) will remain fixed over at least short periods of time and over at least limited changes in scene irradiance during motion. As a result, image frames captured in close proximity in time and space during motion will suffer from identical or at least very similar FPN. Thus, although scene information may change in consecutive image frames, the FPN will stay essentially constant. By averaging, multiple image frames captured during motion will blur the scene information, but will not blur the FPN. As a result, FPN will remain more clearly defined in the blurred image frame provided in block 545 than the scene information.

In one embodiment, 32 or more image frames are accumulated and averaged in blocks 535 and 540. However, any desired number of image frames may be used in other embodiments, but with generally decreasing correction accuracy as frame count is decreased.

Referring now to the defocus-based approach, in block 530, a defocus operation may be performed to intentionally defocus the image frames captured by infrared sensors 132. For example, in one embodiment, one or more actuators 199 may be used to adjust, move, or otherwise translate optical element 180, infrared sensor assembly 128, and/or other components of infrared imaging module 100 to cause infrared sensors 132 to capture a blurred (e.g., unfocused) image frame of the scene. Other non-actuator based techniques are also contemplated for intentionally defocusing infrared image frames such as, for example, manual (e.g., user-initiated) defocusing.

Although the scene may appear blurred in the image frame, FPN (e.g., caused by one or more components of infrared imaging module 100) will remain unaffected by the defocusing operation. As a result, a blurred image frame of the scene will be provided (block 545) with FPN remaining more clearly defined in the blurred image than the scene information.

In the above discussion, the defocus-based approach has been described with regard to a single captured image frame. In another embodiment, the defocus-based approach may include accumulating multiple image frames while the infrared imaging module 100 has been defocused and averaging the defocused image frames to remove the effects of temporal noise and provide a blurred image frame in block 545.

Thus, it will be appreciated that a blurred image frame may be provided in block 545 by either the motion-based approach or the defocus-based approach. Because much of the scene information will be blurred by either motion, defocusing, or both, the blurred image frame may be effectively considered a low pass filtered version of the original captured image frames with respect to scene information.

In block 550, the blurred image frame is processed to determine updated row and column FPN terms (e.g., if row and column FPN terms have not been previously determined then the updated row and column FPN terms may be new row and column FPN terms in the first iteration of block 550). As used in this disclosure, the terms row and column may be used interchangeably depending on the orientation of infrared sensors 132 and/or other components of infrared imaging module 100.

In one embodiment, block 550 includes determining a spatial FPN correction term for each row of the blurred image frame (e.g., each row may have its own spatial FPN correction term), and also determining a spatial FPN correction term for each column of the blurred image frame (e.g., each column may have its own spatial FPN correction term). Such processing may be used to reduce the spatial and slowly varying (1/f) row and column FPN inherent in thermal imagers caused by, for example, 1/f noise characteristics of amplifiers in ROIC 402 which may manifest as vertical and horizontal stripes in image frames.

Advantageously, by determining spatial row and column FPN terms using the blurred image frame, there will be a reduced risk of vertical and horizontal objects in the actual imaged scene from being mistaken for row and column noise (e.g., real scene content will be blurred while FPN remains unblurred).

In one embodiment, row and column FPN terms may be determined by considering differences between neighboring pixels of the blurred image frame. For example, FIG. 6 illustrates differences between neighboring pixels in accordance with an embodiment of the disclosure. Specifically, in FIG. 6 a pixel 610 is compared to its 8 nearest horizontal neighbors: d0-d3 on one side and d4-d7 on the other side. Differences between the neighbor pixels can be averaged to obtain an estimate of the offset error of the illustrated group of pixels. An offset error may be calculated for each pixel in a row or column and the average result may be used to correct the entire row or column.

To prevent real scene data from being interpreted as noise, upper and lower threshold values may be used (thPix and −thPix). Pixel values falling outside these threshold values (pixels dl and d4 in this example) are not used to obtain the offset error. In addition, the maximum amount of row and column FPN correction may be limited by these threshold values.

Further techniques for performing spatial row and column FPN correction processing are set forth in U.S. patent application Ser. No. 12/396,340 filed Mar. 2, 2009 which is incorporated herein by reference in its entirety.

Referring again to FIG. 5, the updated row and column FPN terms determined in block 550 are stored (block 552) and applied (block 555) to the blurred image frame provided in block 545. After these terms are applied, some of the spatial row and column FPN in the blurred image frame may be reduced. However, because such terms are applied generally to rows and columns, additional FPN may remain such as spatially uncorrelated FPN associated with pixel to pixel drift or other causes. Neighborhoods of spatially correlated FPN may also remain which may not be directly associated with individual rows and columns. Accordingly, further proCessing may be performed as discussed below to determine NUC terms.

In block 560, local contrast values (e.g., edges or absolute values of gradients between adjacent or small groups of pixels) in the blurred image frame are determined. If scene information in the blurred image frame includes contrasting areas that have not been significantly blurred (e.g., high contrast edges in the original scene data), then such features may be identified by a contrast determination process in block 560.

For example, local contrast values in the blurred image frame may be calculated, or any other desired type of edge detection process may be applied to identify certain pixels in the blurred image as being part of an area of local contrast. Pixels that are marked in this manner may be considered as containing excessive high spatial frequency scene information that would be interpreted as FPN (e.g., such regions may correspond to portions of the scene that have not been sufficiently blurred). As such, these pixels may be excluded from being used in the further determination of NUC terms. In one embodiment, such contrast detection processing may rely on a threshold that is higher than the expected contrast value associated with FPN (e.g., pixels exhibiting a contrast value higher than the threshold may be considered to be scene information, and those lower than the threshold may be considered to be exhibiting FPN).

In one embodiment, the contrast determination of block 560 may be performed on the blurred image frame after row and column FPN terms have been applied to the blurred image frame (e.g., as shown in FIG. 5). In another embodiment, block 560 may be performed prior to block 550 to determine contrast before row and column FPN terms are determined (e.g., to prevent scene based contrast from contributing to the determination of such terms).

Following block 560, it is expected that any high spatial frequency content remaining in the blurred image frame may be generally attributed to spatially uncorrelated FPN. In this regard, following block 560, much of the other noise or actual desired scene based information has been removed or excluded from the blurred image frame due to: intentional blurring of the image frame (e.g., by motion or defocusing in blocks 520 through 545), application of row and column FPN terms (block 555), and contrast determination (block 560).

Thus, it can be expected that following block 560, any remaining high spatial frequency content (e.g., exhibited as areas of contrast or differences in the blurred image frame) may be attributed to spatially uncorrelated FPN. Accordingly, in block 565, the blurred image frame is high pass filtered. In one embodiment, this may include applying a high pass filter to extract the high spatial frequency content from the blurred image frame. In another embodiment, this may include applying a low pass filter to the blurred image frame and taking a difference between the low pass filtered image frame and the unfiltered blurred image frame to obtain the high spatial frequency content. In accordance with various embodiments of the present disclosure, a high pass filter may be implemented by calculating a mean difference between a sensor signal (e.g., a pixel value) and its neighbors.

In block 570, a flat field correction process is performed on the high pass filtered blurred image frame to determine updated NUC terms (e.g., if a NUC process has not previously been performed then the updated NUC terms may be new NUC terms in the first iteration of block 570).

For example, FIG. 7 illustrates a flat field correction technique 700 in accordance with an embodiment of the disclosure. In FIG. 7, a NUC term may be determined for each pixel 710 of the blurred image frame using the values of its neighboring pixels 712 to 726. For each pixel 710, several gradients may be determined based on the absolute difference between the values of various adjacent pixels. For example, absolute value differences may be determined between: pixels 712 and 714 (a left to right diagonal gradient), pixels 716 and 718 (a top to bottom vertical gradient), pixels 720 and 722 (a right to left diagonal gradient), and pixels 724 and 726 (a left to right horizontal gradient).

These absolute differences may be summed to provide a summed gradient for pixel 710. A weight value may be determined for pixel 710 that is inversely proportional to the summed gradient. This process may be performed for all pixels 710 of the blurred image frame until a weight value is provided for each pixel 710. For areas with low gradients (e.g., areas that are blurry or have low contrast), the weight value will be close to one. Conversely, for areas with high gradients, the weight value will be zero or close to zero. The update to the NUC term as estimated by the high pass filter is multiplied with the weight value.

In one embodiment, the risk of introducing scene information into the NUC terms can be further reduced by applying some amount of temporal damping to the NUC term determination process. For example, a temporal damping factor 2 between 0 and 1 may be chosen such that the new NUC term (NUC_(NEW)) stored is a weighted average of the old NUC term (NUC_(OLD)) and the estimated updated NUC term (NUC_(UPDATE)). In one embodiment, this can be expressed as NUC_(NEW)=λ·NUC_(OLD)+(1−λ)·(NUC_(OLD)+NUC_(UPDATE)).

Although the determination of NUC terms has been described with regard to gradients, local contrast values may be used instead where appropriate. Other techniques may also be used such as, for example, standard deviation calculations. Other types flat field correction processes may be performed to determine NUC terms including, for example, various processes identified in U.S. Pat. No. 6,028,309 issued Feb. 22, 2000, U.S. Pat. No. 6,812,465 issued Nov. 2, 2004, and U.S. patent application Ser. No. 12/114,865 filed May 5, 2008, which are incorporated herein by reference in their entirety.

Referring again to FIG. 5, block 570 may include additional processing of the NUC terms. For example, in one embodiment, to preserve the scene signal mean, the sum of all NUC terms may be normalized to zero by subtracting the NUC term mean from each NUC term. Also in block 570, to avoid row and column noise from affecting the NUC terms, the mean value of each row and column may be subtracted from the NUC terms for each row and column. As a result, row and column FPN filters using the row and column FPN terms determined in block 550 may be better able to filter out row and column noise in further iterations (e.g., as further shown in FIG. 8) after the NUC terms are applied to captured images (e.g., in block 580 further discussed herein). In this regard, the row and column FPN filters may in general use more data to calculate the per row and per column offset coefficients (e.g., row and column FPN terms) and may thus provide a more robust alternative for reducing spatially correlated FPN than the NUC terms which are based on high pass filtering to capture spatially uncorrelated noise.

In blocks 571-573, additional high pass filtering and further determinations of updated NUC terms may be optionally performed to remove spatially correlated FPN with lower spatial frequency than previously removed by row and column FPN terms. In this regard, some variability in infrared sensors 132 or other components of infrared imaging module 100 may result in spatially correlated FPN noise that cannot be easily modeled as row or column noise. Such spatially correlated FPN may include, for example, window defects on a sensor package or a cluster of infrared sensors 132 that respond differently to irradiance than neighboring infrared sensors 132. In one embodiment, such spatially correlated FPN may be mitigated with an offset correction. If the amount of such spatially correlated FPN is significant, then the noise may also be detectable in the blurred image frame. Since this type of noise may affect a neighborhood of pixels, a high pass filter with a small kernel may not detect the FPN in the neighborhood (e.g., all values used in high pass filter may be taken from the neighborhood of affected pixels and thus may be affected by the same offset error). For example, if the high pass filtering of block 565 is performed with a small kernel (e.g., considering only immediately adjacent pixels that fall within a neighborhood of pixels affected by spatially correlated FPN), then broadly distributed spatially correlated FPN may not be detected.

For example, FIG. 11 illustrates spatially correlated FPN in a neighborhood of pixels in accordance with an embodiment of the disclosure. As shown in a sample image frame 1100, a neighborhood of pixels 1110 may exhibit spatially correlated FPN that is not precisely correlated to individual rows and columns and is distributed over a neighborhood of several pixels (e.g., a neighborhood of approximately 4 by 4 pixels in this example). Sample image frame 1100 also includes a set of pixels 1120 exhibiting substantially uniform response that are not used in filtering calculations, and a set of pixels 1130 that are used to estimate a low pass value for the neighborhood of pixels 1110. In one embodiment, pixels 1130 may be a number of pixels divisible by two in order to facilitate efficient hardware or software calculations.

Referring again to FIG. 5, in blocks 571-573, additional high pass filtering and further determinations of updated NUC terms may be optionally performed to remove spatially correlated FPN such as exhibited by pixels 1110. In block 571, the updated NUC terms determined in block 570 are applied to the blurred image frame. Thus, at this time, the blurred image frame will have been initially corrected for spatially correlated FPN (e.g., by application of the updated row and column FPN terms in block 555), and also initially corrected for spatially uncorrelated FPN (e.g., by application of the updated NUC terms applied in block 571).

In block 572, a further high pass filter is applied with a larger kernel than was used in block 565, and further updated NUC terms may be determined in block 573. For example, to detect the spatially correlated FPN present in pixels 1110, the high pass filter applied in block 572 may include data from a sufficiently large enough neighborhood of pixels such that differences can be determined between unaffected pixels (e.g., pixels 1120) and affected pixels (e.g., pixels 1110). For example, a low pass filter with a large kernel can be used (e.g., an N by N kernel that is much greater than 3 by 3 pixels) and the results may be subtracted to perform appropriate high pass filtering.

In one embodiment, for computational efficiency, a sparse kernel may be used such that only a small number of neighboring pixels inside an N by N neighborhood are used. For any given high pass filter operation using distant neighbors (e.g., a large kernel), there is a risk of modeling actual (potentially blurred) scene information as spatially correlated FPN. Accordingly, in one embodiment, the temporal damping factor λ may be set close to 1 for updated NUC terms determined in block 573.

In various embodiments, blocks 571-573 may be repeated (e.g., cascaded) to iteratively perform high pass filtering with increasing kernel sizes to provide further updated NUC terms further correct for spatially correlated FPN of desired neighborhood sizes. In one embodiment, the decision to perform such iterations may be determined by whether spatially correlated FPN has actually been removed by the updated NUC terms of the previous performance of blocks 571-573.

After blocks 571-573 are finished, a decision is made regarding whether to apply the updated NUC terms to captured image frames (block 574). For example, if an average of the absolute value of the NUC terms for the entire image frame is less than a minimum threshold value, or greater than a maximum threshold value, the NUC terms may be deemed spurious or unlikely to provide meaningful correction. Alternatively, thresholding criteria may be applied to individual pixels to determine which pixels receive updated NUC terms. In one embodiment, the threshold values may correspond to differences between the newly calculated NUC terms and previously calculated NUC terms. In another embodiment, the threshold values may be independent of previously calculated NUC terms. Other tests may be applied (e.g., spatial correlation tests) to determine whether the NUC terms should be applied.

If the NUC terms are deemed spurious or unlikely to provide meaningful correction, then the flow diagram returns to block 505. Otherwise, the newly determined NUC terms are stored (block 575) to replace previous NUC terms (e.g., determined by a previously performed iteration of FIG. 5) and applied (block 580) to captured image frames.

FIG. 8 illustrates various image processing techniques of FIG. 5 and other operations applied in an image processing pipeline 800 in accordance with an embodiment of the disclosure. In this regard, pipeline 800 identifies various operations of FIG. 5 in the context of an overall iterative image processing scheme for correcting image frames provided by infrared imaging module 100. In some embodiments, pipeline 800 may be provided by processing module 160 or processor 195 (both also generally referred to as a processor) operating on image frames captured by infrared sensors 132.

Image frames captured by infrared sensors 132 may be provided to a frame averager 804 that integrates multiple image frames to provide image frames 802 with an improved signal to noise ratio. Frame averager 804 may be effectively provided by infrared sensors 132, ROIC 402, and other components of infrared sensor assembly 128 that are implemented to support high image capture rates. For example, in one embodiment, infrared sensor assembly 128 may capture infrared image frames at a frame rate of 240 Hz (e.g., 240 images per second). In this embodiment, such a high frame rate may be implemented, for example, by operating infrared sensor assembly 128 at relatively low voltages (e.g., compatible with mobile telephone voltages) and by using a relatively small array of infrared sensors 132 (e.g., an array of 64 by 64 infrared sensors in one embodiment).

In one embodiment, such infrared image frames may be provided from infrared sensor assembly 128 to processing module 160 at a high frame rate (e.g., 240 Hz or other frame rates). In another embodiment, infrared sensor assembly 128 may integrate over longer time periods, or multiple time periods, to provide integrated (e.g., averaged) infrared image frames to processing module 160 at a lower frame rate (e.g., 30 Hz, 9 Hz, or other frame rates). Further information regarding implementations that may be used to provide high image capture rates may be found in U.S. Provisional Patent Application No. 61/495,879 previously referenced herein.

Image frames 802 proceed through pipeline 800 where they are adjusted by various terms, temporally filtered, used to determine the various adjustment terms, and gain compensated.

In blocks 810 and 814, factory gain terms 812 and factory offset terms 816 are applied to image frames 802 to compensate for gain and offset differences, respectively, between the various infrared sensors 132 and/or other components of infrared imaging module 100 determined during manufacturing and testing.

In block 580, NUC terms 817 are applied to image frames 802 to correct for FPN as discussed. In one embodiment, if NUC terms 817 have not yet been determined (e.g., before a NUC process has been initiated), then block 580 may not be performed or initialization values may be used for NUC terms 817 that result in no alteration to the image data (e.g., offsets for every pixel would be equal to zero).

In blocks 818 and 822, column FPN terms 820 and row FPN terms 824, respectively, are applied to image frames 802. Column FPN terms 820 and row FPN terms 824 may be determined in accordance with block 550 as discussed. In one embodiment, if the column FPN terms 820 and row FPN terms 824 have not yet been determined (e.g., before a NUC process has been initiated), then blocks 818 and 822 may not be performed or initialization values may be used for the column FPN terms 820 and row FPN terms 824 that result in no alteration to the image data (e.g., offsets for every pixel would be equal to zero).

In block 826, temporal filtering is performed on image frames 802 in accordance with a temporal noise reduction (TNR) process. FIG. 9 illustrates a TNR process in accordance with an embodiment of the disclosure. In FIG. 9, a presently received image frame 802 a and a previously temporally filtered image frame 802 b are processed to determine a new temporally filtered image frame 802 e. Image frames 802 a and 802 b include local neighborhoods of pixels 803 a and 803 b centered around pixels 805 a and 805 b, respectively. Neighborhoods 803 a and 803 b correspond to the same locations within image frames 802 a and 802 b and are subsets of the total pixels in image frames 802 a and 802 b. In the illustrated embodiment, neighborhoods 803 a and 803 b include areas of 5 by 5 pixels. Other neighborhood sizes may be used in other embodiments.

Differences between corresponding pixels of neighborhoods 803 a and 803 b are determined and averaged to provide an averaged delta value 805 c for the location corresponding to pixels 805 a and 805 b. Averaged delta value 805 c may be used to determine weight values in block 807 to be applied to pixels 805 a and 805 b of image frames 802 a and 802 b.

In one embodiment, as shown in graph 809, the weight values determined in block 807 may be inversely proportional to averaged delta value 805 c such that weight values drop rapidly towards zero when there are large differences between neighborhoods 803 a and 803 b. In this regard, large differences between neighborhoods 803 a and 803 b may indicate that changes have occurred within the scene (e.g., due to motion) and pixels 802 a and 802 b may be appropriately weighted, in one embodiment, to avoid introducing blur across frame-to-frame scene changes. Other associations between weight values and averaged delta value 805 c may be used in various embodiments.

The weight values determined in block 807 may be applied to pixels 805 a and 805 b to determine a value for corresponding pixel 805 e of image frame 802 e (block 811). In this regard, pixel 805 e may have a value that is a weighted average (or other combination) of pixels 805 a and 805 b, depending on averaged delta value 805 c and the weight values determined in block 807.

For example, pixel 805 e of temporally filtered image frame 802 e may be a weighted sum of pixels 805 a and 805 b of image frames 802 a and 802 b. If the average difference between pixels 805 a and 805 b is due to noise, then it may be expected that the average change between neighborhoods 805 a and 805 b will be close to zero (e.g., corresponding to the average of uncorrelated changes). Under such circumstances, it may be expected that the sum of the differences between neighborhoods 805 a and 805 b will be close to zero. In this case, pixel 805 a of image frame 802 a may both be appropriately weighted so as to contribute to the value of pixel 805 e.

However, if the sum of such differences is not zero (e.g., even differing from zero by a small amount in one embodiment), then the changes may be interpreted as being attributed to motion instead of noise. Thus, motion may be detected based on the average change exhibited by neighborhoods 805 a and 805 b. Under these circumstances, pixel 805 a of image frame 802 a may be weighted heavily, while pixel 805 b of image frame 802 b may be weighted lightly.

Other embodiments are also contemplated. For example, although averaged delta value 805 c has been described as being determined based on neighborhoods 805 a and 805 b, in other embodiments averaged delta value 805 c may be determined based on any desired criteria (e.g., based on individual pixels or other types of groups of sets of pixels).

In the above embodiments, image frame 802 a has been described as a presently received image frame and image frame 802 b has been described as a previously temporally filtered image frame. In another embodiment, image frames 802 a and 802 b may be first and second image frames captured by infrared imaging module 100 that have not been temporally filtered.

FIG. 10 illustrates further implementation details in relation to the TNR process of block 826. As shown in FIG. 10, image frames 802 a and 802 b may be read into line buffers 1010 a and 1010 b, respectively, and image frame 802 b (e.g., the previous image frame) may be stored in a frame buffer 1020 before being read into line buffer 1010 b. In one embodiment, line buffers 1010 a-b and frame buffer 1020 may be implemented by a block of random access memory (RAM) provided by any appropriate component of infrared imaging module 100 and/or host device 102.

Referring again to FIG. 8, image frame 802 e may be passed to an automatic gain compensation block 828 for further processing to provide a result image frame 830 that may be used by host device 102 as desired.

FIG. 8 further illustrates various operations that may be performed to determine row and column FPN terms and NUC terms as discussed. In one embodiment, these operations may use image frames 802 e as shown in FIG. 8. Because image frames 802 e have already been temporally filtered, at least some temporal noise may be removed and thus will not inadvertently affect the determination of row and column FPN terms 824 and 820 and NUC terms 817. In another embodiment, non-temporally filtered image frames 802 may be used.

In FIG. 8, blocks 510, 515, and 520 of FIG. 5 are collectively represented together. As discussed, a NUC process may be selectively initiated and performed in response to various NUC process initiating events and based on various criteria or conditions. As also discussed, the NUC process may be performed in accordance with a motion-based approach (blocks 525, 535, and 540) or a defocus-based approach (block 530) to provide a blurred image frame (block 545). FIG. 8 further illustrates various additional blocks 550, 552, 555, 560, 565, 570, 571, 572, 573, and 575 previously discussed with regard to FIG. 5.

As shown in FIG. 8, row and column FPN teams 824 and 820 and NUC terms 817 may be determined and applied in an iterative fashion such that updated terms are determined using image frames 802 to which previous terms have already been applied. As a result, the overall process of FIG. 8 may repeatedly update and apply such terms to continuously reduce the noise in image frames 830 to be used by host device 102.

Referring again to FIG. 10, further implementation details are illustrated for various blocks of FIGS. 5 and 8 in relation to pipeline 800. For example, blocks 525, 535, and 540 are shown as operating at the normal frame rate of image frames 802 received by pipeline 800. In the embodiment shown in FIG. 10, the determination made in block 525 is represented as a decision diamond used to determine whether a given image frame 802 has sufficiently changed such that it may be considered an image frame that will enhance the blur if added to other image frames and is therefore accumulated (block 535 is represented by an arrow in this embodiment) and averaged (block 540).

Also in FIG. 10, the determination of column FPN terms 820 (block 550) is shown as operating at an update rate that in this example is 1/32 of the sensor frame rate (e.g., normal frame rate) due to the averaging performed in block 540. Other update rates may be used in other embodiments. Although only column FPN terms 820 are identified in FIG. 10, row FPN terms 824 may be implemented in a similar fashion at the reduced frame rate.

FIG. 10 also illustrates further implementation details in relation to the NUC determination process of block 570. In this regard, the blurred image frame may be read to a line buffer 1030 (e.g., implemented by a block of RAM provided by any appropriate component of infrared imaging module 100 and/or host device 102). The flat field correction technique 700 of FIG. 7 may be performed on the blurred image frame.

In view of the present disclosure, it will be appreciated that techniques described herein may be used to remove various types of FPN (e.g., including very high amplitude FPN) such as spatially correlated row and column FPN and spatially uncorrelated FPN.

Other embodiments are also contemplated. For example, in one embodiment, the rate at which row and column FPN term and/or NUC terms are updated can be inversely proportional to the estimated amount of blur in the blurred image frame and/or inversely proportional to the magnitude of local contrast values (e.g., determined in block 560).

In various embodiments, the described techniques may provide advantages over conventional shutter-based noise correction techniques. For example, by using a shutterless process, a shutter (e.g., such as shutter 105) need not be provided, thus permitting reductions in size, weight, cost, and mechanical complexity. Power and maximum voltage supplied to, or generated by, infrared imaging module 100 may also be reduced if a shutter does not need to be mechanically operated. Reliability will be improved by removing the shutter as a potential point of failure. A shutterless process also eliminates potential image interruption caused by the temporary blockage of the imaged scene by a shutter.

Also, by correcting for noise using intentionally blurred image frames captured from a real world scene (not a uniform scene provided by a shutter), noise correction may be performed on image frames that have irradiance levels similar to those of the actual scene desired to be imaged. This can improve the accuracy and effectiveness of noise correction terms determined in accordance with the various described techniques.

As discussed, in various embodiments, infrared imaging module 100 may be configured to operate at low voltage levels. In particular, infrared imaging module 100 may be implemented with circuitry configured to operate at low power and/or in accordance with other parameters that permit infrared imaging module 100 to be conveniently and effectively implemented in various types of host devices 102, such as mobile devices and other devices.

For example, FIG. 12 illustrates a block diagram of another implementation of infrared sensor assembly 128 including infrared sensors 132 and an LDO 1220 in accordance with an embodiment of the disclosure. As shown, FIG. 12 also illustrates various components 1202, 1204, 1205, 1206, 1208, and 1210 which may implemented in the same or similar manner as corresponding components previously described with regard to FIG. 4. FIG. 12 also illustrates bias correction circuitry 1212 which may be used to adjust one or more bias voltages provided to infrared sensors 132 (e.g., to compensate for temperature changes, self-heating, and/or other factors).

In some embodiments, LDO 1220 may be provided as part of infrared sensor assembly 128 (e.g., on the same chip and/or wafer level package as the ROIC). For example, LDO 1220 may be provided as part of an FPA with infrared sensor assembly 128. As discussed, such implementations may reduce power supply noise introduced to infrared sensor assembly 128 and thus provide an improved PSRR. In addition, by implementing the LDO with the ROIC, less die area may be consumed and fewer discrete die (or chips) are needed.

LDO 1220 receives an input voltage provided by a power source 1230 over a supply line 1232. LDO 1220 provides an output voltage to various components of infrared sensor assembly 128 over supply lines 1222. In this regard, LDO 1220 may provide substantially identical regulated output voltages to various components of infrared sensor assembly 128 in response to a single input voltage received from power source 1230.

For example, in some embodiments, power source 1230 may provide an input voltage in a range of approximately 2.8 volts to approximately 11 volts (e.g., approximately 2.8 volts in one embodiment), and LDO 1220 may provide an output voltage in a range of approximately 1.5 volts to approximately 2.8 volts (e.g., approximately 2.5 volts in one embodiment). In this regard, LDO 1220 may be used to provide a consistent regulated output voltage, regardless of whether power source 1230 is implemented with a conventional voltage range of approximately 9 volts to approximately 11 volts, or a low voltage such as approximately 2.8 volts. As such, although various voltage ranges are provided for the input and output voltages, it is contemplated that the output voltage of LDO 1220 will remain fixed despite changes in the input voltage.

The implementation of LDO 1220 as part of infrared sensor assembly 128 provides various advantages over conventional power implementations for FPAs. For example, conventional FPAs typically rely on multiple power sources, each of which may be provided separately to the FPA, and separately distributed to the various components of the FPA. By regulating a single power source 1230 by LDO 1220, appropriate voltages may be separately provided (e.g., to reduce possible noise) to all components of infrared sensor assembly 128 with reduced complexity. The use of LDO 1220 also allows infrared sensor assembly 128 to operate in a consistent manner, even if the input voltage from power source 1230 changes (e.g., if the input voltage increases or decreases as a result of charging or discharging a battery or other type of device used for power source 1230).

The various components of infrared sensor assembly 128 shown in FIG. 12 may also be implemented to operate at lower voltages than conventional devices. For example, as discussed, LDO 1220 may be implemented to provide a low voltage (e.g., approximately 2.5 volts). This contrasts with the multiple higher voltages typically used to power conventional FPAs, such as: approximately 3.3 volts to approximately 5 volts used to power digital circuitry; approximately 3.3 volts used to power analog circuitry; and approximately 9 volts to approximately 11 volts used to power loads. Also, in some embodiments, the use of LDO 1220 may reduce or eliminate the need for a separate negative reference voltage to be provided to infrared sensor assembly 128.

Additional aspects of the low voltage operation of infrared sensor assembly 128 may be further understood with reference to FIG. 13. FIG. 13 illustrates a circuit diagram of a portion of infrared sensor assembly 128 of FIG. 12 in accordance with an embodiment of the disclosure. In particular, FIG. 13 illustrates additional components of bias correction circuitry 1212 (e.g., components 1326, 1330, 1332, 1334, 1336, 1338, and 1341) connected to LDO 1220 and infrared sensors 132. For example, bias correction circuitry 1212 may be used to compensate for temperature-dependent changes in bias voltages in accordance with an embodiment of the present disclosure. The operation of such additional components may be further understood with reference to similar components identified in U.S. Pat. No. 7,679,048 issued Mar. 16, 2010 which is hereby incorporated by reference in its entirety. Infrared sensor assembly 128 may also be implemented in accordance with the various components identified in U.S. Pat. No. 6,812,465 issued Nov. 2, 2004 which is hereby incorporated by reference in its entirety.

In various embodiments, some or all of the bias correction circuitry 1212 may be implemented on a global array basis as shown in FIG. 13 (e.g., used for all infrared sensors 132 collectively in an array). In other embodiments, some or all of the bias correction circuitry 1212 may be implemented an individual sensor basis (e.g., entirely or partially duplicated for each infrared sensor 132). In some embodiments, bias correction circuitry 1212 and other components of FIG. 13 may be implemented as part of ROIC 1202.

As shown in FIG. 13, LDO 1220 provides a load voltage Vload to bias correction circuitry 1212 along one of supply lines 1222. As discussed, in some embodiments, Vload may be approximately 2.5 volts which contrasts with larger voltages of approximately 9 volts to approximately 11 volts that may be used as load voltages in conventional infrared imaging devices.

Based on Vload, bias correction circuitry 1212 provides a sensor bias voltage Vbolo at a node 1360. Vbolo may be distributed to one or more infrared sensors 132 through appropriate switching circuitry 1370 (e.g., represented by broken lines in FIG. 13). In some examples, switching circuitry 1370 may be implemented in accordance with appropriate components identified in U.S. Pat. Nos. 6,812,465 and 7,679,048 previously referenced herein.

Each infrared sensor 132 includes a node 1350 which receives Vbolo through switching circuitry 1370, and another node 1352 which may be connected to ground, a substrate, and/or a negative reference voltage. In some embodiments, the voltage at node 1360 may be substantially the same as Vbolo provided at nodes 1350. In other embodiments, the voltage at node 1360 may be adjusted to compensate for possible voltage drops associated with switching circuitry 1370 and/or other factors.

Vbolo may be implemented with lower voltages than are typically used for conventional infrared sensor biasing. In one embodiment, Vbolo may be in a range of approximately 0.2 volts to approximately 0.7 volts. In another embodiment, Vbolo may be in a range of approximately 0.4 volts to approximately 0.6 volts. In another embodiment, Vbolo may be approximately 0.5 volts. In contrast, conventional infrared sensors typically use bias voltages of approximately 1 volt.

The use of a lower bias voltage for infrared sensors 132 in accordance with the present disclosure permits infrared sensor assembly 128 to exhibit significantly reduced power consumption in comparison with conventional infrared imaging devices. In particular, the power consumption of each infrared sensor 132 is reduced by the square of the bias voltage. As a result, a reduction from, for example, 1.0 volt to 0.5 volts provides a significant reduction in power, especially when applied to many infrared sensors 132 in an infrared sensor array. This reduction in power may also result in reduced self-heating of infrared sensor assembly 128.

In accordance with additional embodiments of the present disclosure, various techniques are provided for reducing the effects of noise in image frames provided by infrared imaging devices operating at low voltages. In this regard, when infrared sensor assembly 128 is operated with low voltages as described, noise, self-heating, and/or other phenomena may, if uncorrected, become more pronounced in image frames provided by infrared sensor assembly 128.

For example, referring to FIG. 13, when LDO 1220 maintains Vload at a low voltage in the manner described herein, Vbolo will also be maintained at its corresponding low voltage and the relative size of its output signals may be reduced. As a result, noise, self-heating, and/or other phenomena may have a greater effect on the smaller output signals read out from infrared sensors 132, resulting in variations (e.g., errors) in the output signals. If uncorrected, these variations may be exhibited as noise in the image frames. Moreover, although low voltage operation may reduce the overall amount of certain phenomena (e.g., self-heating), the smaller output signals may permit the remaining error sources (e.g., residual self-heating) to have a disproportionate effect on the output signals during low voltage operation.

To compensate for such phenomena, infrared sensor assembly 128, infrared imaging module 100, and/or host device 102 may be implemented with various array sizes, frame rates, and/or frame averaging techniques. For example, as discussed, a variety of different array sizes are contemplated for infrared sensors 132. In some embodiments, infrared sensors 132 may be implemented with array sizes ranging from 32 by 32 to 160 by 120 infrared sensors 132. Other example array sizes include 80 by 64, 80 by 60, 64 by 64, and 64 by 32. Any desired array size may be used.

Advantageously, when implemented with such relatively small array sizes, infrared sensor assembly 128 may provide image frames at relatively high frame rates without requiring significant changes to ROIC and related circuitry. For example, in some embodiments, frame rates may range from approximately 120 Hz to approximately 480 Hz.

In some embodiments, the array size and the frame rate may be scaled relative to each other (e.g., in an inversely proportional manner or otherwise) such that larger arrays are implemented with lower frame rates, and smaller arrays are implemented with higher frame rates. For example, in one embodiment, an array of 160 by 120 may provide a frame rate of approximately 120 Hz. In another embodiment, an array of 80 by 60 may provide a correspondingly higher frame rate of approximately 240 Hz. Other frame rates are also contemplated.

By scaling the array size and the frame rate relative to each other, the particular readout timing of rows and/or columns of the FPA may remain consistent, regardless of the actual FPA size or frame rate. In one embodiment, the readout timing may be approximately 63 microseconds per row or column.

As previously discussed with regard to FIG. 8, the image frames captured by infrared sensors 132 may be provided to a frame averager 804 that integrates multiple image frames to provide image frames 802 (e.g., processed image frames) with a lower frame rate (e.g., approximately 30 Hz, approximately 60 Hz, or other frame rates) and with an improved signal to noise ratio. In particular, by averaging the high frame rate image frames provided by a relatively small FPA, image noise attributable to low voltage operation may be effectively averaged out and/or substantially reduced in image frames 802. Accordingly, infrared sensor assembly 128 may be operated at relatively low voltages provided by LDO 1220 as discussed without experiencing additional noise and related side effects in the resulting image frames 802 after processing by frame averager 804.

Other embodiments are also contemplated. For example, although a single array of infrared sensors 132 is illustrated, it is contemplated that multiple such arrays may be used together to provide higher resolution image frames (e.g., a scene may be imaged across multiple such arrays). Such arrays may be provided in multiple infrared sensor assemblies 128 and/or provided in the same infrared sensor assembly 128. Each such array may be operated at low voltages as described, and also may be provided with associated ROIC circuitry such that each array may still be operated at a relatively high frame rate. The high frame rate image frames provided by such arrays may be averaged by shared or dedicated frame averagers 804 to reduce and/or eliminate noise associated with low voltage operation. As a result, high resolution infrared images may be obtained while still operating at low voltages.

In various embodiments, infrared sensor assembly 128 may be implemented with appropriate dimensions to permit infrared imaging module 100 to be used with a small form factor socket 104, such as a socket used for mobile devices. For example, in some embodiments, infrared sensor assembly 128 may be implemented with a chip size in a range of approximately 4.0 mm by approximately 4.0 mm to approximately 5.5 mm by approximately 5.5 mm (e.g., approximately 4.0 mm by approximately 5.5 mm in one example). Infrared sensor assembly 128 may be implemented with such sizes or other appropriate sizes to permit use with socket 104 implemented with various sizes such as: 8.5 mm by 8.5 mm, 8.5 mm by 5.9 mm, 6.0 mm by 6.0 mm, 5.5 mm by 5.5 mm, 4.5 mm by 4.5 mm, and/or other socket sizes such as, for example, those identified in Table 1 of U.S. Provisional Patent Application No. 61/495,873 previously referenced herein.

Referring now to FIG. 14, a block diagram is shown of another implementation of host system 102 showing how system 102 may include one or more non-thermal imaging modules such as visible light camera module 1406 in addition to one or more infrared imaging modules such as infrared imaging module 100 in accordance with an embodiment of the disclosure. System 102 may be used to monitor a real-world scene such as scene 1430 (e.g., a scene including a track having boundaries).

System 102 may include one or more infrared imaging modules 100, one or more visible light cameras 1406, and additional components as described above in connection with FIG. 1 (e.g., processor 195, memory 196, display 197, one or more motion sensors 194, and/or other components 198 such as a control panel, alert components, or communications components). In various embodiments, components of system 102 of FIG. 14 may be implemented in the same or similar manner as corresponding components of host device 102 of FIG. 1. Moreover, components of system 102 may be configured to perform various NUC processes and other processes described herein.

As shown in FIG. 14, in some embodiments, infrared imaging module 100 may include various optical elements 1403 (e.g., one or more infrared-transmissive lens, one or more infrared-transmissive prisms, one or more infrared-reflective mirrors, or one or more infrared fiber optic elements) that guide infrared radiation from scene 1430 to an FPA of infrared imaging module 100. In some embodiments, optical elements 1403 may be used to suitably define or alter FOV 1404 of infrared imaging module 100. A switchable FOV (e.g., selectable by infrared imaging module 100 and/or processor 195) may optionally be provided, which may be useful when, for example, a selective close-up view of a portion of scene 1430 is desired.

Optical elements 1403 may also include one or more filters adapted to pass infrared radiation of some wavelengths but substantially block infrared radiation of other wavelengths (e.g., short-wave infrared (SWIR) filters, mid-wave infrared (MWIR) filters, long-wave infrared (LWIR) filters, and narrow-band filters). Such filters may be utilized to tailor infrared imaging module 100 for increased sensitivity to a desired band of infrared wavelengths. For example, in some situations, it may be desirable to detect exhaled breaths of a person or an animal. In this type of situation, a better result may be achieved by utilizing a narrow-band filter that transmits only in the wavelengths matching a specific absorption/emission spectrum of carbon dioxide (CO₂) or other constituent gases of an exhaled breath. In some embodiments, filters may be selectable (e.g., provided as a selectable filter wheel). In other embodiments, filters may be fixed as appropriate for a desired application of system 102.

Visible light camera 1406 may be a small form factor non-thermal imaging module or imaging device, and may be implemented in a similar manner as various embodiments of infrared imaging module 100 disclosed herein, but with one or more sensors responsive to non-thermal radiation (e.g., radiation in the visible, near infrared, short-wave infrared or other non-thermal portion of the electromagnetic spectrum). For example, in some embodiments, visible light camera 1406 may be implemented with a charge-coupled device (CCD) sensor, an electron multiplying CCD (EMCCD) sensor, a complementary metal-oxide-semiconductor (CMOS) sensor, a scientific CMOS (sCMOS) sensor, an intensified charge-coupled device (ICCD), or other sensors.

As shown in FIG. 14, in some embodiments, visible light camera module 1406 may include various optical elements 1405 (e.g., one or more lenses, one or more color filters, one or more prisms, one or more mirrors, or one or more fiber optic elements) that guide non-thermal radiation from scene 1430 to visible light camera module 1406. In some embodiments, optical elements 1405 may be used to suitably define or alter FOV 1407 of visible light camera module 1406. A switchable FOV (e.g., selectable by visible light camera module 1406 and/or processor 195) may optionally be provided, which may be useful when, for example, a selective close-up view of a portion of scene 1430 is desired. If desired, elements 1403 and 1405 may be operable to alternately switch between an infrared imaging mode and a visible light imaging mode for system 102.

Optical elements 1405 may also include one or more filters adapted to pass radiation of some wavelengths (colors) but substantially block radiation of other wavelengths (e.g., red color filters, blue color filters, green color filters, near-infrared color filters, short-wave infrared filters, and narrow-band filters). In some embodiments, filters of elements 1405 may be selectable (e.g., provided as a selectable filter wheel). In other embodiments, filters of element 1405 may be fixed as appropriate for a desired application of system 102. Although camera module 1406 is sometimes referred to herein as a visible light camera module as an example, it should be appreciated that camera module 1406 may be any suitable non-thermal camera module as described herein that generates images in response to incoming light having any suitable corresponding range of non-thermal wavelengths (e.g., visible light wavelengths, near infrared wavelengths, short-wave infrared wavelengths or other wavelengths that are relatively shorter than thermal infrared wavelengths).

In some embodiments, non-thermal images such as visible light images captured by visible light camera 1406 may be received by processor 195, which may be configured to fuse, superimpose, or otherwise combine the visible light images with the thermal images captured by infrared imaging module 100 as further described herein.

In some embodiments, visible light camera 1406 may be co-located with infrared imaging module 100 in a housing structure and oriented so that FOV 1407 of visible light camera 1406 at least partially overlaps FOV 1404 of infrared imaging module 100. In one example, infrared imaging module 100 and visible light camera 1406 may be implemented as a dual sensor module sharing a common substrate according to various techniques described in U.S. Provisional Patent Application No. 61/748,018 previously referenced herein. Such a dual sensor module implementation may include common circuitry and/or common restraint devices for infrared imaging and visible light imaging, thereby potentially reducing an overall size of system 102 as compared to embodiments where infrared imaging module 100 and visible light camera 1406 are implemented as individual modules. Additionally, the dual sensor module implementation may be adapted to reduce a parallax error between images captured by infrared imaging module 100 and visible light camera 1406 by reducing the distance between them.

Infrared images captured, processed, and/or otherwise managed by infrared imaging module 100 may be radiometrically normalized infrared images (e.g., thermal images). That is, pixels that make up the captured image may contain calibrated thermal data (e.g., temperature data). As discussed above in connection with FIG. 1, infrared imaging module 100 and/or associated components may be calibrated using appropriate techniques so that images captured by infrared imaging module 100 are properly calibrated thermal images. In some embodiments, appropriate calibration processes may be performed periodically by infrared imaging module 100 and/or processor 195 so that infrared imaging module 100, and hence the thermal images captured by it, may maintain proper calibration.

Radiometric normalization permits infrared imaging module 100 and/or processor 195 to efficiently detect, from thermal images, objects having a specific range of temperature. Infrared imaging module 100 and/or processor 195 may detect such objects (e.g., a boundary of a track) efficiently and effectively, because thermal images of objects having a specific temperature may be easily discernible from a background and other objects, and yet less susceptible to lighting conditions or obscuring.

Also referring to FIG. 15, an example thermal image 1530 (shown as a user-viewable thermal image for ease of understanding) that may be captured by infrared imaging module 100 is shown. As this example thermal image shows, a portion of a track boundary 1501 may exhibit a different temperature or emit more or less radiation at the same temperature than a background such as a portion 1503 of a track and a portion 1504 of a surface outside the track. Thus, various features of a boundary such as boundary 1501 that is detected in a thermal image such as image 1530 may be accurately and yet efficiently differentiated and tracked using appropriate detection and tracking operations described herein and elsewhere. For example, processor 195 may perform edge detection operations on thermal image 1530 to detect edges 1502 of boundary 1501. The location of boundary 1501 within sequentially captured images may also be used to determine that boundary 1501 is moving relative to the vehicle in, for example, direction 1501 and/or at a particular speed.

In some embodiments, if visible light images captured by visible light camera 1406 are available, processor 195 may be configured to track features of a scene such as such as the boundary of a track for a remote controlled vehicle based additionally or alternatively on the visible light images. For example, the visible light images may provide more detail and contrast than the thermal images in certain ambient light conditions, and thus may be analyzed using suitable boundary detection algorithms in such favorable light conditions. In another example, both the visible light images and the thermal images may be analyzed to complementarily increase detection and tracking accuracy. In another example, the thermal images and the visible light images may be combined or fused as further described herein, and the combined or fused images may be analyzed to track the features of the scene. If processor 195 is configured to detect and track the features of a scene using the visible light images, processor 195 may be further configured to convert pixel coordinates of the tracked features in the visible light images to corresponding pixel coordinates in the thermal images.

In some embodiments, thermal images from one or more infrared imaging modules such as infrared imaging module 100 and non-thermal images from one or more non-thermal camera modules such as visible light camera module 1406 may be fused or combined to generate images having a higher definition, contrast, and/or detail.

The fusing or combining operations in accordance with one or more embodiments may be described in further detail with reference to FIG. 16, which is a flowchart of a process 1600 to combine or fuse the thermal images and the non-thermal (e.g., visible light) images. The combined images may include radiometric data and/or other infrared characteristics corresponding to scene 1430, but with significantly more object detail (e.g., contour or edge detail) and/or contrast than typically provided by the thermal or non-thermal images alone. Thus, for example, the combined images generated in these examples may beneficially provide sufficient radiometric data, detail, and contrast to allow easier recognition and/or interpretation of the presence, location, position, or other features of objects such as track boundaries in scene 1430.

Although the process described herein in connection with FIG. 16 discusses fusing or combining thermal images with visible light images as an example, it should be appreciated that the process may be applied to combining thermal images with any suitable non-thermal images (e.g., visible light images, near infrared images, short-wave infrared images, EMCCD images, ICCD images, or other non-thermal images).

At block 1602, visible light images and infrared images such as thermal images May be received. For example, visible light images of scene 1430 may be captured by visible light camera 1406 and the captured visible light images may be received by processor 195. Processor 195 may perform various operations of process 1600 using both thermal images and non-thermal images, for example.

At block 1604, high spatial frequency content from one or more of the visible light and thermal images may be derived from one or more of the visible light and thermal images received in block 1602. High spatial frequency content derived according to various embodiments may include edge/contour details and/or high contrast pixels extracted from the one or more of the visible light and thermal images, for example.

In one embodiment, high spatial frequency content may be derived from the received images by performing a high pass filter (e.g., a spatial filter) operation on the images, where the result of the high pass filter operation is the high spatial frequency content. In an alternative embodiment, high spatial frequency content may be derived from the received images by performing a low pass filter operation on the images, and then subtracting the result from the original images to get the remaining content, which is the high spatial frequency content. In another embodiment, high spatial frequency content may be derived from a selection of images through difference imaging, for example, where one image is subtracted from a second image that is perturbed from the first image in some fashion, and the result of the subtraction is the high spatial frequency content. For example, optical elements 1403 of infrared imaging module 100 and/or optical elements 1405 of visible light camera 1406 may be configured to introduce vibration, de-focusing, and/or movement artifacts into a series of images captured by one or both of infrared imaging module 100 and visible light camera 1406. High spatial frequency content may be derived from subtractions of images such as adjacent images in the series.

In some embodiments, high spatial frequency content may be derived from only the visible light images or the thermal images. In other embodiments, high spatial frequency content may be derived from only a single visible light or thermal image. In further embodiments, high spatial frequency content may be derived from one or more components of the visible light and/or thermal images, such as a luminance component of visible light images, for example, or a radiometric component of thermal images. Resulting high spatial frequency content may be stored temporarily (e.g., in memory 196) and/or may be further processed according to block 1608.

At block 1606, one or more thermal images may be de-noised. For example, processor 195 may be configured to de-noise, smooth, or blur one or more thermal images of scene 1430 using a variety of image processing operations. In one embodiment, removing high spatial frequency noise from the thermal images allows the processed thermal images to be combined with high spatial frequency content derived according to block 1604 with significantly less risk of introducing double edges (e.g., edge noise) to objects depicted in combined images of scene 1430.

In one embodiment, removing noise from the thermal mages may include performing a low pass filter (e.g., a spatial and/or temporal filter) operation on the images, where the result of the low pass filter operation is de-noised or processed thermal images. In a further embodiment, removing noise from one or more thermal images may include down-sampling the thermal images and then up-sampling the images back to the original resolution.

In another embodiment, processed thermal images may be derived by actively blurring thermal images of scene 1430. For example, optical elements 1403 may be configured to slightly de-focus one or more thermal images captured by infrared imaging module 100. The resulting intentionally blurred thermal images may be sufficiently de-noised or blurred so as to reduce or eliminate a risk of introducing double edges into combined images of scene 1430, as further described below. In other embodiments, blurring or smoothing image processing operations may be performed by processor 195 on the received thermal images as an alternative or supplement to using optical elements 1403 to actively blur thermal images of scene 1430. Resulting processed thermal images may be stored temporarily (e.g., in memory 196) and/or may be further processed according to block 1608.

At block 1608, high spatial frequency content may be blended with one or more thermal images. For example, processor 195 may be configured to blend high spatial frequency content derived in block 1604 with one or more thermal images of scene 1430, such as the processed thermal images provided in block 1606.

In one embodiment, high spatial frequency content may be blended with thermal images by superimposing the high spatial frequency content onto the thermal images, where the high spatial frequency content replaces or overwrites those portions of the thermal images corresponding to where the high spatial frequency content exists. For example, the high spatial frequency content may include edges of objects depicted in images of scene 1430, but may not exist within the interior of such objects. In such embodiments, blended image data may simply include the high spatial frequency content, which may subsequently be encoded into one or more components of combined images, as described in block 1610.

For example, a radiometric component of thermal images may be a chrominance component of the thermal images, and the high spatial frequency content may be derived from the luminance and/or chrominance components of visible light images. In this embodiment, combined images may include the radiometric component (e.g., the chrominance component of the thermal images) encoded into a chrominance component of the combined images and the high spatial frequency content directly encoded (e.g., as blended image data but with no thermal image contribution) into a luminance component of the combined images. By doing so, a radiometric calibration of the radiometric component of the thermal images may be retained. In similar embodiments, blended image data may include the high spatial frequency content added to a luminance component of the thermal images, and the resulting blended data encoded into a luminance component of resulting combined images.

In other embodiments, high spatial frequency content may be derived from one or more particular components of one or a series of visible light and/or thermal images, and the high spatial frequency content may be encoded into corresponding one or more components of combined images. For example, the high spatial frequency content may be derived from a luminance component of visible spectrum images, and the high spatial frequency content, which in this embodiment is all luminance image data, may be encoded into a luminance component of combined images.

In another embodiment, high spatial frequency content may be blended with thermal images using a blending parameter and an arithmetic equation. For example, in one embodiment, the high spatial frequency content may be derived from a luminance component of visible light images. In such an embodiment, the high spatial frequency content may be blended with a corresponding luminance component of thermal image according to a blending parameter and a blending equation to produce blended image data. The blended image data may be encoded into a luminance component of combined images, for example, and the chrominance component of the thermal images may be encoded into the chrominance component of the combined images. In embodiments where the radiometric component of the infrared images may be their chrominance component, the combined images may retain a radiometric calibration of the thermal images. In other embodiments, portions of the radiometric component may be blended with the high spatial frequency content and then encoded into combined images.

More generally, the high spatial frequency content may be derived from one or more components of visible light image and/or thermal image. In such an embodiment, the high spatial frequency content may be blended with one or more components of the thermal images to produce blended image data (e.g., using a blending parameter and a blending equation), and resulting combined images may include the blended image data encoded into corresponding one or more components of the combined images. In some embodiments, the one or more components of the blended data do not have to correspond to the eventual one or more components of the combined images (e.g., a color space/format conversion may be performed as part of an encoding process).

A blending parameter value may be selected by a user or may be automatically determined by processor 195 according to context or other data, for example, or according to an image enhancement level expected by system 102. In some embodiments, the blending parameter may be adjusted or refined while combined images are being displayed (e.g., by display 197). In some embodiments, a blending parameter may be selected such that blended image data includes only thermal characteristics, or, alternatively, only visible light characteristics. A blending parameter may also be limited in range, for example, so as not to produce blended data that is out-of-bounds with respect to a dynamic range of a particular color space/format or a display.

In addition to or as an alternative to the processing described above, processing according to the high contrast mode may include one or more processing steps, ordering of processing steps, arithmetic combinations, and/or adjustments to blending parameters as disclosed in U.S. patent application Ser. No. 13/437,645 previously referenced herein. For example, the following equations may be used to determine the components Y, Cr and Cb for the combined images with the Y component from the high pass filtered visible light images and the Cr and Cb components from the thermal images.

hp_(—) y_vis=highpass(y_vis)

(y _(—) ir,cr _(—) ir,cb _(—) ir)=colored(lowpass(ir_signal_linear))

In the above equations, highpass(y_vis) may be high spatial frequency content derived from high pass filtering a luminance component of visible light images. Colored(lowpass(ir_signal_linear)) may be the resulting luminance and chrominance components of the thermal images after the thermal images are low pass filtered. In some embodiments, the thermal images may include a luminance component that is selected to be 0.5 times a maximum luminance (e.g., of a display and/or a processing step). In related embodiments, the radiometric component of the thermal images may be the chrominance component of the thermal images. In some embodiments, the y_ir component of the thermal images may be dropped and the components of the combined images may be (hp_y_vis, cr_ir, cb_ir), using the notation above.

In another embodiment, the following equations may be used to determine the components Y, Cr and Cb for combined images with the Y component from the high pass filtered visible light images and the Cr and Cb components from the thermal images.

comb_(—) y=y _(—) ir+alpha×hp_(—) y_vis

comb_(—) cr=cr _(—) ir

comb_(—) cb=cb _(—) ir

The variation of alpha thus gives the user an opportunity to decide how much contrast is needed in the combined images. With an alpha of close to zero, the thermal images alone will be shown, but with a very high alpha, very sharp contours/edges can be seen in the combined images. Theoretically, alpha can be an infinitely large number, but in practice a limitation will probably be necessary, to limit the size of alpha that can be chosen to what will be convenient in the current application.

Once the high spatial frequency content is blended with one or more thermal images, processing may proceed to block 1610, where blended data may be encoded into components of the combined images in order to form the combined images.

At block 1610, the blended data may be encoded into one or more components of the combined images. For example, processor 195 may be configured to encode blended data derived or produced in accordance with block 1608 into combined images that increases, refines, or otherwise enhances the information conveyed by either the visible light or thermal images viewed by themselves. In some embodiments, encoding blended image data into a component of combined images may include additional image processing operations, for example, such as dynamic range adjustment, normalization, gain and offset operations, noise reduction, and color space conversions, for instance.

In addition, processor 195 may be configured to encode other image data into combined images. For example, if blended image data is encoded into a luminance component of combined images, a chrominance component of either visible light images or thermal images may be encoded into a chrominance component of combined images. Selection of source images may be made through user input, for example, or may be determined automatically based on context or other data.

More generally, in some embodiments, a component of combined images that is not encoded with blended data may be encoded with a corresponding component of visible light images or thermal images. By doing so, a radiometric calibration of thermal images and/or a color space calibration of visible light images may be retained in the resulting combined images.

In some embodiments, at least some part or some functionalities of processor 195 described herein may be implemented as part of infrared imaging modules 100, for example, at processing module 160 described above in connection with FIG. 3. In some embodiments, at least some part or some functionalities of processor 195 may be part of or implemented with other existing processors of an external device such as a mobile phone, a tablet device, a mobile handset, a laptop computer, a desktop computer, an automobile information display system, or any other devices that may be used to present monitoring information from a monitoring system. In other embodiments, processor 195 may interface and communicate with such other external processors and components associated with such processors.

In one suitable configuration that is sometimes discussed herein as an example, system 102 may be implemented as a motion controlled device such as image-assisted remote control vehicle 1700 of FIG. 17. As shown in FIG. 17, image-assisted remote control vehicle 1700 may include one or more motion controlled components such as motion controlled component 1701 and one or more infrared imaging modules 100. Image-assisted remote control vehicle 1700 may, for example, be a remote control vehicle with one or more infrared imaging modules 100 that each has a field of view (FOV) that covers a portion of a scene such as scene 1430.

In one embodiment, image-assisted remote control vehicle 1700 may be a remote control vehicle such as a small scale remote control car. However, this is merely illustrative. If desired, image-assisted remote control vehicle 1700 may implemented having various sizes and may be another type of remote control vehicle such as a remote control truck, tank, boat, airplane, helicopter, or any other suitable remote control vehicle having one or more infrared imaging devices mounted or integrated.

Image-assisted remote control vehicle 1700 includes a motion controlled component 1701 that controls the motion of image-assisted remote control vehicle 1700. Motion controlled component 1701 may include wheels, propellers, or other propulsion, steering, or driving systems or components. Motion controlled component 1701 may be powered by a battery, such as battery 1704, or by a separate power source.

As shown in FIG. 17, image-assisted remote control vehicle 1700 may include one or more visible light cameras 1406, one or more motion sensors 194, one or more batteries such as battery 1704, memory such as memory 196, one or more processors such as processor 195, and communications components such as wired or wireless communications components 1706.

Battery 1704, in an embodiment, may be a lithium ion battery, a lithium polymer battery, a nickel cadmium battery, a nickel metal hydride battery, or other suitable type of battery technology for a remote control vehicle. System 1700 may include one, two, three, or more than three batteries or, if desired, system 1700 may be powered by an external battery or battery pack (e.g., through a wired connection to a battery in a backpack or other portable vessel). In other embodiments, an engine, such as an internal combustion engine, may be used additionally or alternatively to battery 1704.

Memory 196 may include one or more memory devices to store data and information, including infrared images and monitoring information. The one or more memory devices may include various types of memory for infrared image and other information storage including volatile and non-volatile memory devices, such as RAM (Random Access Memory), ROM (Read-Only Memory), EEPROM (Electrically-Erasable Read-Only Memory), flash memory, and/or a disk drive. In one embodiment, thermal images and monitoring information stored in the one or more memory devices may be retrieved later for purposes of reviewing and/or further diagnosing the conditions of the environment monitored by device 1700. In various embodiments, processor 195 may be configured to execute software instructions stored on memory 196 to perform various methods, processes, or operations in the manner described herein.

Display 197 may be configured to present, indicate, or otherwise convey monitoring information generated by processor 195, infrared imaging modules 100, and/or visible light cameras 1406. In various embodiments, display 197 may be implemented with an electronic display screen, such as a liquid crystal display (LCD), a cathode ray tube (CRT), light-emitting-diode (LED) or various other types of generally known video displays and monitors. Display 197 according to such embodiments may be suitable for presenting user-viewable thermal images converted by processor 195 from thermal images captured by infrared imaging modules 100.

In some embodiments, existing display screens on external devices such as mobile phones, tablet devices, laptop computers, desktop computers, automobile information display systems, or any other devices may receive the thermal images and/or the monitoring information from image-assisted remote control vehicle 1700 to present the monitoring information to a user.

In this regard, communications components 1706 may be configured to handle, manage, or otherwise facilitate wired and/or wireless communication between various components of image-assisted remote control vehicle and between image-assisted remote control vehicle and an external device. For example, image-assisted remote control vehicle 1700 may transmit and receive data to and from a control device, such as control device 1802 as shown in FIG. 18, through communications components 1706. The control device may transmit operation instructions to image-assisted remote control vehicle 1700, and may receive and further process raw/processed thermal images and/or monitoring information for presentation to a user through communications components 1706 configured to manage wired and/or wireless connections. In another example, image-assisted remote control vehicle 1700 may further transmit data to another external device with a display, which may also receive and further process raw/processed thermal images and/or monitoring information for presentation to a user through communications components 1706.

In various embodiments, communications components 1706 may include a wireless communication component (e.g., based on the IEEE 802.11 WiFi standards, the Bluetooth™ standard, the ZigBee™ standard, or other appropriate short range wireless communication standards), a wireless broadband component (e.g., based on WiMax technologies), mobile cellular component, a wireless satellite component, or other appropriate wireless communication components. Communication module 1706 may also be configured for a proprietary wireless communication protocol and interface based on radio frequency (RF), microwave frequency (MWF), infrared frequency (IRF), and/or other appropriate wireless transmission technologies. Communications components 1706 may include an antenna coupled thereto for wireless communication purposes. Thus, in one example, communications components 1706 may handle, manage, or otherwise facilitate wireless communication by establishing wireless link to control device 1902, another external device (e.g., mobile phone, a tablet device, a mobile handset, a laptop computer, a desktop computer, etc.), or other appropriate wireless networking devices.

In various embodiments, communications components 1706 may be configured to interface with a wired network via a wired communication component such as an Ethernet interface, a power-line modem, a Digital Subscriber Line (DSL) modem, a Public Switched Telephone Network (PSTN) modem, a cable modem, and/or other appropriate components for wired communication. Proprietary wired communication protocols and interfaces may also be supported by communication module 1706. Communications components 1706 may be configured to communicate over a wired link (e.g., through a network router, switch, hub, or other network devices) for wired communication purposes. For example, a wired link may be implemented with a power-line cable, a coaxial cable, a fiber-optics cable, or other appropriate cables or wires that support corresponding wired network technologies.

In some embodiments, image-assisted remote control vehicle may include as many such communication components 1706 as desired for various applications of image-assisted remote control vehicle to suit various types of monitoring environments. In other embodiments, communication components 1706 may be integrated into or implemented as part of various other components of image-assisted remote control vehicle 1700. For example, infrared imaging module 100, processor 195, and display 197 may each include a subcomponent that may be configured to perform the operations of communications components 1706, and may communicate via wired and/or wireless connection without separate components 1706.

Processor 195 may be configured to receive infrared image data captured by infrared imaging modules 100 and/or visible light camera 1406. Processor 195 may be configured to perform, on the received infrared images of a scene, various infrared image processing and analysis operations as further described herein, for example, to detect and track a boundary, and determine various attributes associated with the boundary. Processor 195 may be configured to collect, compile, analyze, or otherwise process output images or analyzed boundary data of the infrared image processing and analysis operations to generate monitoring information such as boundary detection and/or tracking information. Processor 195 may determine, using the boundary data, operation modification information to be provided for controlling motion controlled components 1701 to modify the operation (e.g., the speed, power, direction, etc.) of a remote control vehicle.

Motion sensors 194 may be monitored by and provide information to infrared imaging modules 100 and/or processor 195 for performing various NUC techniques described herein. Motion sensors 194 may be accessed by processor 195 to determine one or more spatial characteristics of a speed, orientation, and/or acceleration of image-assisted remote control vehicle 1700. The spatial characteristics may be used when adjusting an operation of image-assisted remote control vehicle 1700.

In various embodiments, one or more components of image-assisted remote control vehicle 1700 may be combined and/or implemented or not, as desired or depending on application requirements. For example, processor 195 may be combined with infrared imaging modules 100, memory 196, and/or communications components 1706. In another example, processor 195 may be combined with infrared imaging modules 100 with only certain operations of processor 195 performed by circuitry (e.g., processor, logic device, microprocessor, microcontroller, etc.) within infrared imaging modules 100.

Infrared imaging modules 100 of image-assisted remote control vehicle 1700 may be configured to capture, process, and/or otherwise manage infrared images (e.g., including thermal images) of a scene such as scene 1430 (see FIG. 14). In this regard, infrared imaging modules 100 may be attached, mounted, installed, or otherwise disposed at any suitable location on or within device 1700 that allows at least a portion of the scene to be placed within field of view (FOV) 1404 of each infrared imaging module 100.

FIG. 18 is a block diagram showing how image-assisted remote control vehicle 1700 may be communicatively coupled to control device 1802 (sometimes referred to herein as a remote control or remote control device). As shown in FIG. 18, system 1800 may include image-assisted remote control vehicle 1700 and control device 1802. Image-assisted remote control vehicle 1700 may be communicatively coupled to control device 1802 over communications path 1808 (e.g., a wireless radio-frequency communications path). In some embodiments, image-assisted remote control vehicle 1700 may communicate with control device 1802 through an antenna such as antenna 1804 and/or through a network such as network 1806 (e.g., a closed proprietary network or a global network such as the Internet).

For example, control device 1802 may transmit signals to antenna 1804 over path 1834 (e.g., wired or wireless communications paths) and antenna 1804 may transmit some or all of the received signals to image-assisted remote control vehicle 1700 over path 1812 (e.g., a wired or wireless communications path). As another example, control device 1802 may transmit signals to network 1806 over path 1836, network 1806 may transmit some or all of the received signals to antenna 1804 over path 1835 (e.g., a wired or wireless communications path) and image-assisted remote control vehicle 1700 may receive information associated with the signals over path 1812 (e.g., a wired or wireless communications path).

Control device 1802 may be located in a common geographical location with image-assisted remote control vehicle 1700 or may be located remote from image-assisted remote control vehicle 1700. For example, control device 1802 may be a mobile device such as a smartphone, wireless cellular phone, satellite phone, computing tablet (e.g., iPad™ from Apple®), wearable computing device, smartwatch (e.g., Galaxy Gear™ from Samsung®), eyeglasses with appropriate computer hardware resources (e.g., Google Glass™ from Google®) and/or other types of computing devices that communicates with image-assisted remote control vehicle 1700 over a network or a combination of multiple networks. In another example, control device 1802 is a separate remote control device configured to communicate with image-assisted remote control vehicle 1700.

Control device 1802 may include various computing modules suitable for communicating with devices 1700, for generating signals including operation instructions, and for processing and storing images and/or other monitoring information received from device 1700. Control device 1802 may include video analytics application 1814, one or more displays 1816, storage such as memory 1818, processing equipment such as processor 1820, communications components such as communications module 1822, control components such as control panel 1828, input components such as input components 1830 and/or output components such as output components 1832. In one embodiment, processor 1820 may be an implementation of processor 195. Communications module 1822 may include one or more antennas 1824 and additional communications circuitry 1826 (e.g., radio-frequency front end circuitry, signal generation circuitry, modulation circuitry, etc.). Input components 1830 may include a microphone, a keyboard, a touchscreen, a mouse, and/or other components suitable for receiving user input. Output components 1832 may include one or more speakers, headphones, or other output components.

If visible light images of the scene are available (e.g., captured by visible light camera 1406), processor 1820 may be configured to superimpose, fuse, blend, or otherwise combine the infrared images and the visible light images to detect track boundaries and/or to generate user-viewable image having a higher definition and/or contrast, and present such image on display 1816 of control device 1802. For example, processor 1820 may be configured to generate images that are combined images including radiometric data and/or other infrared characteristics corresponding to scene but with significantly more boundary detail (e.g., contour and/or edge detail) and/or contrast than typically provided by the infrared or visible light images alone, as further described herein. In another example, combined images may include radiometric data and visible light characteristics (e.g., a visible spectrum color) corresponding to one or more boundaries in scene, as described for appropriate embodiments disclosed in various patent applications referenced herein such as, for example, U.S. Patent Application Nos. 61/473,207, 61/746,069, 61/746,074, 61/792,582, 61/793,952, Ser. No. 12/766,739, 13/105,765, or 13/437,645, or International Patent Application No. PCT/EP2011/056432, or others as appropriate. Combined images generated in these examples may provide sufficient radiometric data, edge detail, and contrast to allow easier recognition and/or interpretation of the presence, location, and position of the track and boundaries.

Video analytics application 1814, in an embodiment, detects boundaries of a track using infrared images and/or non-thermal images and generates instructions for modifying the operation of image-assisted remote control vehicle 1700. In an example, video analytics application 1814 may receive infrared images from vehicle 1700 and modify operation instructions entered by a user on input components 1830. In another example, a video analytics application running on image-assisted remote control vehicle 1700 may be used to modify operation instructions received from control device 1802.

In one embodiment, one or more infrared imaging modules 100 may be mounted on image-assisted remote control vehicle 1700 of FIG. 17. In the example of FIG. 19, image-assisted remote control vehicle 1700 of FIG. 17 is implemented as remote control vehicle 1901. Image-assisted remote control vehicle 1700 that has been implemented as an image-assisted remote control vehicle of the type shown in FIG. 19 may be provided with one or more imaging modules 1904. For example, imaging module 1904 may be placed on the lower front portion of remote control vehicle 1901 and face the front as in FIG. 19. Imaging module 1904 may include one or more infrared imaging modules 100 and/or one or more visible light camera 1406.

Image-assisted remote control vehicle 1700 may include imaging module 1904 in a housing such as housing 1906. In some embodiments, housing 1906 may include clamps, clips, suction cups, or other suitable attachment mechanisms to releasably attach housing 1906, and hence imaging components (e.g., infrared imaging modules 100), to a suitable motion controlled device such as remote control vehicle 1901. In some embodiments, housing 1906 may be fixedly attached to a mounting structure with an appropriate fastener. Housing 1906 may optionally be an integral portion of the housing of the vehicle and may protrude from the vehicle as shown or may be mounted flush with a surface of the housing of the vehicle (e.g., in an opening or behind an infrared transparent window).

Additional components such as processor 195, communications components 1706, and memory 196 may be located within housing 1906 or within other portions of remote control vehicle 1901.

Image-assisted remote control vehicle 1700 may be placed on track 1930 with boundaries 1932. In various embodiments, a user may create track 1930 by placing or drawing boundaries 1932 on a surface such as an indoor floor or an outdoor ground surface. For example, the user may place or draw boundaries 1932 to create track 1930 with boundaries on each side of image-assisted remote control vehicle 1700 to form a lane in any desired configuration such as the loop shown in the example of FIG. 19. Boundaries 1902 may include any material having different emissivity, temperatures, color, or other visible and/or thermal properties from the surface on which the boundaries are placed. Materials with different emissivity emit thermal radiation differently at the same temperature. Material with high emissivity (e.g., paint, concrete, wood, etc.) emits thermal radiation more strongly than material with low emissivity (e.g., aluminum, silver, copper, etc.) when they are at the same temperature. The different emissivity may be reflected in infrared images, for example, with material with low emissivity shown as darker than material with high emissivity. In an embodiment, boundaries 1932 include material with low emissivity, such as electrical tape or wire, or aluminum tape or wire. Boundaries 1932 having low emissivity are particularly suited where the surface has high emissivity, such as on concrete or wood surfaces. In other embodiments, boundaries 1902 include material with high emissivity, such as paint or wood sticks. Boundaries 1932 having high emissivity may be particularly suited where the surface has low emissivity, such as metal surfaces.

As shown in the embodiment of FIG. 19, control device 1802 may be implemented as a mobile device that can be held by a hand such as hand 1912 of a user. Control device 1802 may be utilized by the user to control image-assisted remote control vehicle 1700 by sending operation instructions to image-assisted remote control vehicle 1700. For example, the user may control the direction and speed of image-assisted remote control vehicle 1700 by inputting direction and speed instructions on a control panel, a GUI presented on a touchscreen, a joystick, knob, button or combination of buttons, or other input component 1830. In an embodiment, control device 1802 receives infrared images from image-assisted remote control vehicle 1700.

Imaging module 1904 including one or more infrared imaging modules 100 may capture infrared images of a part of track 1930. One or more processors which may be located in image-assisted control vehicle 1700, control device 1910, or both, detects boundaries 1932 based on infrared images. Boundaries 1932 may be detected based on image differences resulting from, for example, differences in the emissivities of boundaries 1932 and the rest of the surface of track 1930. For example, boundaries 1932 may appear as relatively darker or relatively brighter areas in a captured infrared image that other areas of the image. Processor 195, in some embodiments, performs edge-detection to determine a more accurate location of boundaries 1932. In further examples, the detection may be performed using one or more image analysis operations (e.g., video analytics), which may include scene reconstruction operations and/or boundary tracking operations.

In one embodiment, image-assisted remote control vehicle 1700 (e.g., processor 195) may generate image data such as infrared images from one or more imaging modules 1904 and detect, in the infrared images, a contiguous line or block of pixels (also referred to as an edge, a line or a continuous line) corresponding to a boundary or potential boundary (e.g., a boundary formed from a material having a low relatively low emissivity, such as the emissivity of electrical tape or aluminum tape or having a different emissivity from the background scene materials or objects). Such an edge may indicate a presence of a boundary in the vicinity of device 1700, and may be analyzed further as described herein to ascertain the presence of the boundary, track the boundary, determine the location of the boundary relative to the vehicle, determine the orientation of the boundary relative to the vehicle, and/or determine various other attributes associated with the detected boundary

Referring now to FIG. 20, a flowchart is illustrated of a process 2000 for detecting boundaries, such as boundary 1932, and modifying operation of image-assisted remote control vehicle such as image-assisted remote control vehicle 1700 based on the detected boundaries.

At block 2002, images, such as infrared images (e.g., images containing pixels with radiometric data), of a scene that includes a part of a track may be captured by one or more imaging modules (e.g., infrared imaging modules 100 on motion controlled device such as remote control vehicle 1901). The imaging modules are mounted on or within a remote control vehicle, such as infrared-assisted remote control vehicle 1700, which is remotely operated based on operation instructions received form a control device, such as control device 1802. The captured infrared images may be radiometrically calibrated infrared images as described above in connection with infrared imaging module 100. If desired, visible light images may also be captured at block 2002 using visible light cameras 1406 on a motion controlled device such as remote control vehicle 1901.

At block 2004, processing operations may be performed on the captured infrared images. Processing operations may include NUC corrections, other noise corrections, calibration operations, smoothing operations, filtering operations, edge detection operations, perspective calibration operations or other image processing operations. Additional processing operations may also be performed on visible light images optionally captured at block 2002. In some embodiments, image processing operations performed at block 2004 may include combining or fusing infrared images and visible light images as described above in connection with FIGS. 14 and 16 (as examples). NUC correction processes may be performed on the captured infrared images to remove noise therein, for example, by using various NUC techniques disclosed herein.

In some embodiments, the captured infrared images may be scaled and/or perspective calibrated. Scaling and perspective calibration operations may be performed manually or automatically using scaling or perspective calibration data stored in memory 196 and/or sensor data such as data indicating the orientation of the vehicle.

At block 2006, one or more boundaries, such as boundary 1932, may be detected using the processed infrared images. Detecting the boundaries may include identifying image pixels in the infrared images that correspond to the boundary (e.g., due to image intensity differences resulting from boundary material differences such as emissivity differences from the surrounding materials, due to detected temperatures differences in a radiometrically calibrated thermal image resulting from differences in temperature of the boundary material from the surrounding materials, due to a detected edge in an image corresponding to an edge of a boundary material) or otherwise identifying characteristics of an image that correspond to the desired boundary to be detected.

For example, various analysis and processing operations may be performed using the captured infrared images to detect and/or track boundaries, and determine various attributes, such as a distance to a boundary, associated with the detected boundaries. In one embodiment, regions of contiguous pixels having different intensity or temperature values from other regions may be detected in images such as infrared images (e.g., uncalibrated or radiometrically calibrated infrared images) to detect boundaries. For example, the detection operation may using image pixel value differences to differentiate boundaries having a surface emissivity distribution that is different from the rest of the track. In another embodiment, regions of contiguous pixels having temperature values in a specific range may be detected from radiometrically calibrated infrared images to detect boundaries. The infrared images and boundaries detected therein may be further processed and/or analyzed, for example, by performing various filtering operations and analyzing the size, shape, and/or infrared characteristics of boundaries, to detect boundaries, and to further localize boundaries.

In another embodiment, the infrared images may be analyzed to detect one or more candidate boundaries, for example, using background modeling techniques, edge detection techniques, or other boundary detection techniques suitable for use with infrared images. The size and shape and/or emissive characteristics of the candidate boundaries may then be analyzed to determine whether they correspond to those of boundaries that may be present in the scene. For example, a detected object in an image such as another vehicle on the track or a surface feature of the track surface may initially be detected as a candidate boundary. However, it may be determined that the detected object has a shape or other characteristic (e.g., a non-linear shape or edge or an orientation, intensity, or temperature that is different from previously detected portions of a track boundary) that indicates that the detected object does not have the characteristics of a boundary and thus is not a boundary. The radiometric properties (e.g., temperature) of the candidate boundaries may also be analyzed, so that the detection may be ascertained based on the size, the shape, and the radiometric properties of the detected candidates. As described above, further processing and analysis operations may be performed if needed to localize and track the features of boundaries. Boundary detection using infrared images may be less susceptible to false detection of spurious boundaries compared with boundary detection techniques using visible light images and may facilitate custom design and relatively easy and inexpensive track construction using common household materials such as electrical or aluminum tape. However, this is merely illustrative. In some embodiments, track boundary materials may be provided having particularly desirable thermal characteristics for detection in infrared images.

In one aspect of this embodiment, background modeling techniques may be used to detect boundaries in the scene. Because the background (e.g., the track surface) of the scene may have different emissive properties than that of the boundaries, and because infrared images are generally insensitive to changing lighting conditions, a background model (e.g., pixel values that correspond to a background material) may be constructed, and a region of pixels different from the background (also referred to as a “region of interest”) in a particular image may easily be distinguished as a candidate foreground boundary. As described above, the size, shape, and/or radiometric properties of such a region of interest (ROI) may then be analyzed to further ascertain whether the detected ROI likely represent a boundary. In an embodiment, image-assisted remote control vehicle 1700 may be configured to calculate an approximate emissivity or temperature of the boundary by performing other appropriate processing and analysis operations on the infrared images and the radiometric data contained therein.

In various embodiments, the various processing and analysis operations described for block 2006 may be omitted or included, and may be performed in any other order as appropriate for detecting boundaries. For example, in some embodiments, detecting a linearly extending feature in the infrared images may be sufficient to detect and track boundary in a scene, whereas in other embodiments various infrared image analytics may be performed in combination to increase the accuracy of the detection.

In some embodiments, if visible light images are available (e.g., captured by visible light camera 1406), operations for block 2006 may additionally or alternatively involve performing suitable boundary detection and tracking algorithms on the visible light images or combined images of the visible light images and the infrared images. If the detection and tracking of boundaries are performed using the visible light images, operations for block 2006 may further involve converting pixel coordinates of the tracked boundaries in the visible light images to corresponding pixel coordinates in the infrared images. Other appropriate techniques for detecting boundaries in the infrared images by analyzing the infrared images, visible light images, and/or combined images may also be utilized for block 2006.

At block 2008, the location of one or more detected boundaries, such as boundary 1932, may be determined. In an embodiment, the location of the boundary is determined relative to image-assisted remote control vehicle 1700. Determining the location of a detected boundary may include determining which of several imaging modules captured an image in which the boundary was detected. In some embodiments, determining the location of the boundary may include determining the location of the boundary within an image captured by a given imaging module in order to further localize the determined location. Determining the location of the boundary within a captured image may include determining which pixels in the image include the boundary. Determining the location of the detected boundary may include determining an angular position of the boundary in a reference frame of image-assisted remote control vehicle 1700. For example, a reference frame of image-assisted remote control vehicle 1700 may have an angular position of zero degrees for forward locations directly in front of image-assisted remote control vehicle 1700. In this example, a boundary directly below image-assisted remote control vehicle 1700 may have an angular position of plus or minus 90 degrees. However, this is merely illustrative. If desired, angular positions of detected boundaries may be determined in any suitable reference frame or coordinate system such as a reference frame that is fixed to the physical environment surrounding image-assisted remote control vehicle 1700.

Determining the location of the detected boundary may also include determining a distance to the boundary from image-assisted remote control vehicle 1700. For example, determining the distance to the boundary may include determining the distance to the boundary using stored scale and/or perspective calibration data. In another example, determining the distance to the boundary may include determining a thickness of the boundary in an image, determining a thickness of boundary, and computing a distance based on the thickness of the boundary in the image and the thickness of the boundary. In an embodiment, the thickness may be determined by an average thickness of aluminum or wire tape that is used as boundaries (e.g., 1 cm, 5 cm, 10 cm, etc.). In another embodiment, the thickness may be determined based on perspective calibration data and thickness data collected while on the track. In a further embodiment, the thickness may be determined by user input.

In an embodiment, additional boundary information may be determined at block 2008 by further analysis and processing and/or during the processing and analysis performed for detection. For example, the orientation of the boundary relative to image-assisted remote control vehicle 1700 or the speed at which the vehicle is approaching the boundary may be determined by analyzing and processing the infrared images.

In some embodiments, if visible light images captured by visible light cameras 1406 in imaging modules 1804 are available, image-assisted remote control vehicle 1700 may be configured to track features of a scene such as one or more boundaries and/or the orientation of the one or more boundaries based additionally or alternatively on the visible light images. For example, the visible light images may provide more detail and contrast than the infrared images in certain ambient light conditions, and thus may be analyzed using suitable boundary tracking algorithms in such favorable light conditions. In another example, both the visible light images and the infrared images may be analyzed to complementarily increase detection and tracking accuracy. In another example, the infrared images and the visible light images may be combined or fused as further described herein, and the combined or fused images may be analyzed to track the features of the scene. If image-assisted remote control vehicle 1700 is configured to detect and track the features of a scene using the visible light images, processor 195 may be further configured to convert pixel coordinates of the tracked features in the visible light images to corresponding pixel coordinates in the infrared images.

At block 2010, operation of image-assisted remote control vehicle 1700 may be modified based on the detected boundary (e.g., if it is determined, based on the determined location and distance to the boundary) that the image-assisted remote control vehicle 1700 is approaching the boundary). In an embodiment, a processor such as processor 195 is configured to modify the operation by reducing the speed of image-assisted remote control vehicle 1700 when it approaches the boundary. When the processor determines that image-assisted remote control vehicle 1700 is approaching the boundary based on the infrared image, the speed may be reduced, for example, to give the user more time to turn the vehicle and stay within the track.

In other embodiments, a processor such as processor 195 may to modify the operation of image-assisted remote control vehicle 1700 by steering away from the boundary when the vehicle approaches the boundary. When the processor 195 determines that image-assisted remote control vehicle 1700 is approaching the boundary based on the infrared image, the direction of travel may be adjusted by an angle that is actively determined to keep the vehicle within the track and/or to give the user more time to turn the vehicle. The adjustment of the direction of travel may be based on the determined orientation of the boundary. For example, when the boundary is perpendicular to the direction of travel such that image-assisted remote control vehicle 1700 is heading straight into the boundary, the direction of travel may be adjusted by a large angle (e.g., a 90 degree turn) and/or the speed may be drastically reduced (e.g., by reducing power to the wheels or by braking the vehicle). In another example, when the boundary is at a 45 degree angle with respect to the direction of travel, image-assisted remote control vehicle 1700 may avoid the boundary by a relatively smaller turn, so the direction of travel may be adjusted by a smaller angle (e.g., a 45 degree turn) and the speed may be reduced by a relatively smaller amount.

In further embodiments, processor such as processor 195 is configured to determine a lane based on one or more infrared images, determine an angle of the lane, and modify the operation of image-assisted remote control vehicle 1700 by reducing the angle between the lane and the direction of travel. The lane may be defined by two boundaries detected based on the infrared images, with each of the two boundaries located on opposite sides of image-assisted remote control vehicle 1700. The angle of the lane may be defined by the average angle of the two boundaries, for example, with respect to the direction of travel. When the processor determines that image-assisted remote control vehicle 1700 is traveling towards a boundary based on the infrared image, the direction of travel is adjusted by an angle so that the direction of travel is closer to parallel the lane. The adjustment of the direction of travel may be a function of the angle of the lane. For example, when the lane is at a 45 degree angle with respect to the direction of travel, image-assisted remote control vehicle 1700 may turn the direction of travel so that the angle of the lane with respect to the direction of travel is reduced (e.g., 0-30 degrees).

The modification of operation may be based on the distance to the detected boundary. For example, a speed reduction and/or a modification to the direction of travel of image-assisted remote control vehicle 1700 may be inversely proportional or otherwise based on the distance to the boundary (e.g., when it is determined that the vehicle is relatively close to the boundary, the speed may be reduced to a relatively greater extent and/or the direction of travel may be adjusted to a relatively greater extent than corresponding adjustments when it is determined that the vehicle is relatively further from the boundary). In some embodiments, the operation may only be modified when it is determined that the vehicle is within a predetermined distance from the boundary.

In certain embodiments, processor such as processor 195 is configured to modify the operation of image-assisted remote control vehicle by a combination of the above methods. For example, processor 195 may reduce speed and change the direction of travel of image-assisted remote control vehicle 1700 when image-assisted remote control vehicle 1700 is approaching the boundary.

In various embodiments, processor 195 utilizes a video analytics application 1814 to determine the modification of operation, such as the type of modification (e.g., reduce speed, change direction, etc.) and the extent of the modification. One or more processors such as processor 195 may be located in image-assisted control vehicle 1700, control device 1802, or both. The processor that determines the modification of operation may be the same processor or a different processor from the processor that detects boundaries 1932 based on infrared images. In some embodiments, the processor that determines modification of operation is located on image-assisted control vehicle 1700 and directly modifies operation of image-assisted control vehicle 1700. In other embodiments, the processor that determines modification of operation is located on control device 1802 and modifies operation of image-assisted control vehicle 1700 by sending modification instructions to image-assisted control vehicle 1700.

At block 2012, when image-assisted remote control vehicle 1700 is determined from the images not to be approaching the boundary, a processor such as processor 195 continues to monitor the scene using continuously captured images such as infrared images and/or visible light images.

Although various image processing techniques have been described, any of the various processing techniques set forth in any of the patent applications referenced herein may be used. For example, in some embodiments, visible images and/or infrared images may be blended or otherwise combined in accordance with any of the techniques set forth in U.S. Patent Application Nos. 61/473,207, 61/746,069, 61/746,074, 61/792,582, 61/793,952, Ser. No. 12/766,739, 13/105,765, or 13/437,645, or International Patent Application No. PCT/EP2011/056432, or others as appropriate.

Where applicable, various embodiments provided by the present disclosure can be implemented using hardware, software, or combinations of hardware and software. Also where applicable, the various hardware components and/or software components set forth herein can be combined into composite components comprising software, hardware, and/or both without departing from the spirit of the present disclosure. Where applicable, the various hardware components and/or software components set forth herein can be separated into sub-components comprising software, hardware, or both without departing from the spirit of the present disclosure. In addition, where applicable, it is contemplated that software components can be implemented as hardware components, and vice-versa.

Software in accordance with the present disclosure, such as non-transitory instructions, program code, and/or data, can be stored on one or more non-transitory machine readable mediums. It is also contemplated that software identified herein can be implemented using one or more general purpose or specific purpose computers and/or computer systems, networked and/or otherwise. Where applicable, the ordering of various steps described herein can be changed, combined into composite steps, and/or separated into sub-steps to provide features described herein.

Embodiments described above illustrate but do not limit the invention. It should also be understood that numerous modifications and variations are possible in accordance with the principles of the invention. Accordingly, the scope of the invention is defined only by the following claims. 

What is claimed is:
 1. A system, comprising: a remote control vehicle; an infrared imaging module mounted on the remote control vehicle, wherein the infrared imaging module is configured to capture an infrared image of a part of a track; and a Processor configured to communicate with the infrared imaging module, wherein the processor is configured to: detect a boundary of the track using the infrared image; and modify an operation of the remote control vehicle based on the boundary.
 2. The system of claim 1, further comprising a control device, wherein the processor is further configured to: receive operation instructions from the control device; and modify the operation of the remote control vehicle based on the operation instructions and the boundary.
 3. The system of claim 2, wherein the processor is disposed within the remote control vehicle and is further configured to transmit the infrared image to the control device.
 4. The system of claim 2, wherein the control device comprises a mobile device.
 5. The system of claim 1, wherein the processor is further configured to determine a location of the boundary and an orientation of the boundary relative to the remote control vehicle.
 6. The system of claim 1, wherein the remote control vehicle comprises a motion sensor configured to determine a speed of the remote control vehicle, and wherein the processor is configured to modify the operation of the remote control vehicle based on the speed of the remote control vehicle.
 7. The system of claim 1, wherein the processor is configured to modify the operation of the remote control vehicle by reducing a speed of the remote control vehicle when the remote control vehicle approaches the boundary.
 8. The system of claim 1, wherein the processor is configured to modify the operation of the remote control vehicle by steering away from the boundary when the remote control vehicle approaches the boundary.
 9. The system of claim 1, wherein the processor is further configured to: determine a lane defined by the boundary and an additional boundary, wherein the boundary and the additional boundary are located on opposing sides of the remote control vehicle, and modify the operation of the remote control vehicle by reducing an angle between the lane and a direction of travel of the remote control vehicle.
 10. The system of claim 1, wherein the boundary comprises electrical tape or aluminum tape.
 11. The system of claim 1, wherein the infrared imaging module comprises an infrared microbolometer array.
 12. The system of claim 1, further comprising at least one non-thermal imaging module mounted on the remote control vehicle.
 13. A method, comprising: capturing an infrared image of a part of the track with an infrared imaging module on a remote control vehicle; detecting a boundary of the track in the infrared image; and modifying an operation of the remote control vehicle based on the boundary.
 14. The method of claim 13, further comprising receiving operation instructions from a control device.
 15. The method of claim 14, further comprising transmitting the infrared image to the control device.
 16. The method of claim 14, further comprising modifying the operation of the remote control vehicle based on the operation instructions and the boundary.
 17. The method of claim 13, further comprising determining a location and an orientation of the boundary relative to the remote control vehicle.
 18. The method of claim 13, wherein the modifying comprises reducing a speed of the remote control vehicle when the remote control vehicle approaches the boundary.
 19. The method of claim 13, wherein the modifying comprises steering away from the boundary when the remote control vehicle approaches the boundary.
 20. The method of claim 13, further comprising: determining, based on the infrared image, a distance between the boundary and the remote control vehicle; determining, based on the infrared image, a speed at which the remote control vehicle is approaching the boundary; and determining, based on the infrared image, an orientation of the boundary relative to a direction of travel of the remote control vehicle. 